Financial_Analysis_Course_Week_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dop0NyqVarpQ"
      },
      "source": [
        "# **Week 1: Introduction to Financial Data Analysis and Python Basics (Beginner Level)**\n",
        "\n",
        "## **Contents**\n",
        "- Numpy\n",
        "- Pandas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQpsrdMDX00r"
      },
      "source": [
        "## Numpy in Finance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWH5rwof_-Rt",
        "outputId": "387ead61-4851-4b07-9893-072304c7af6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.77914227 0.73948427 0.28437597 0.78569466 0.18355496 0.28246366\n",
            " 0.96992758 0.07564049 0.03796548 0.5766245 ]\n"
          ]
        }
      ],
      "source": [
        "# Generating Random Numbers\n",
        "import numpy as np\n",
        "\n",
        "random_numbers = np.random.rand(10)  # Generate 1000 random numbers from a uniform distribution between 0 and 1\n",
        "print(random_numbers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pxXDbRWLFDVK",
        "outputId": "fa9ffb82-15b4-4c23-c54b-b394f8a80637"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n"
          ]
        }
      ],
      "source": [
        "#  Reshaping Arrays\n",
        "import numpy as np\n",
        "\n",
        "data = np.array([1, 2, 3, 4, 5, 6])\n",
        "reshaped_data = np.reshape(data, (2, 3))\n",
        "print(reshaped_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ft1VwiZiP9my",
        "outputId": "933c5c9b-aa42-4e3b-91e5-77a5e73083bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.005999999999999998\n",
            "0.032619012860600184\n"
          ]
        }
      ],
      "source": [
        "# Computing Mean and Standard Deviation\n",
        "import numpy as np\n",
        "\n",
        "returns = np.array([0.05, -0.02, 0.03, 0.01, -0.04])\n",
        "mean_return = np.mean(returns)\n",
        "# standard deviation measures the fluctuation in the fund's share price or returns over time, that how much it away from mean.\n",
        "std_deviation = np.std(returns) # It's not Alpha or Beta in Finance\n",
        "\n",
        "print(mean_return)\n",
        "print(std_deviation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "X27xUHefYFal",
        "outputId": "27bf8b91-c4d9-4f18-f27a-be50868d38cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "COV-  0.00047\n",
            "CORR-  0.8497831579263382\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Calculating Covariance and Correlation\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "stock_returns = np.array([0.05, -0.02, 0.03, 0.01, -0.04])\n",
        "market_returns = np.array([0.02, 0.01, 0.03, 0.02, -0.01])\n",
        "\n",
        "# covariance (-inf to +inf) can predict how two stocks might perform relative to each other in the future. It determine if stocks' returns tend to move with or against each other.\n",
        "covariance = np.cov(stock_returns, market_returns)[0, 1]\n",
        "\n",
        "# Scaled version of covariance as it does not affect by change in scale and limit from -1 to +1.\n",
        "correlation = np.corrcoef(stock_returns, market_returns)[0, 1]\n",
        "print(\"COV- \",covariance)\n",
        "print(\"CORR- \",correlation)\n",
        "\n",
        "plt.plot(stock_returns)\n",
        "plt.plot(market_returns)\n",
        "plt.title(\"Stock Vs Market Returns\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "3u8z8jvKd3SF",
        "outputId": "a6ad8492-f325-4ebc-87b1-1c524b39f041"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.04741069 0.05442479 0.03741865 0.03847234 0.0591147  0.05805019\n",
            " 0.02591801 0.01869803 0.05792194 0.03022023 0.0634055  0.07097582]\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Simulating Asset Returns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "mean = 0.05\n",
        "std_dev = 0.02\n",
        "num_periods = 12\n",
        "\n",
        "asset_returns = np.random.normal(mean, std_dev, num_periods)\n",
        "print(asset_returns)\n",
        "\n",
        "plt.plot(list(range(num_periods)), asset_returns)\n",
        "plt.xlabel(\"Period\")  # add X-axis label\n",
        "plt.ylabel(\"Asset Returns\")  # add Y-axis label\n",
        "plt.title(\"Asset Returns Simulation\")  # add title\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FVcysVIZeOap",
        "outputId": "1ca1dc58-96c6-4afb-8229-0e2bb477d81c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 6  8]\n",
            " [10 12]]\n",
            "-----------\n",
            "[[19 22]\n",
            " [43 50]]\n"
          ]
        }
      ],
      "source": [
        "# Performing Matrix Operations\n",
        "import numpy as np\n",
        "\n",
        "matrix_a = np.array([[1, 2], [3, 4]])\n",
        "matrix_b = np.array([[5, 6], [7, 8]])\n",
        "\n",
        "matrix_sum = np.add(matrix_a, matrix_b)\n",
        "matrix_product = np.dot(matrix_a, matrix_b)\n",
        "print(matrix_sum)\n",
        "print(\"-----------\")\n",
        "print(matrix_product)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgrobpWagUtx",
        "outputId": "60bc1210-2d66-4104-d34b-3077775b81fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-91911.85600739928\n"
          ]
        }
      ],
      "source": [
        "# Calculating Present Value\n",
        "import numpy_financial as nf\n",
        "rate = 0.06\n",
        "nper = 10\n",
        "pmt = -200\n",
        "fv = 200000\n",
        "\n",
        "# Present value (PV) is the current value of a future sum of money or stream of cash flows given a specified rate of return.\n",
        "present_value = nf.pv(rate/12, nper*12, pmt, fv)\n",
        "print(present_value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "o7DbluHC9ugk",
        "outputId": "a0738753-5bbf-4b71-9055-b74e6db4068c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1.01124421 1.02118894 1.00151892 0.98853205 0.9897496  1.01623911\n",
            " 1.00059432 1.02368863 1.00315149 0.98330079 0.99460988 0.99355924]\n",
            "[101.12442066 103.2671396  103.42399426 102.23793353 101.18995378\n",
            " 102.83318869 102.89430473 105.33173012 105.66368166 103.89918135\n",
            " 103.33915202 102.67356979]\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Simulating Geometric Brownian Motion (GBM)\n",
        "import numpy as np\n",
        "\n",
        "initial_price = 100.0\n",
        "drift = 0.01\n",
        "volatility = 0.05\n",
        "num_periods = 12\n",
        "\n",
        "# Geometric Brownian motion is a widely used mathematical model for asset prices with the assumption of their constant volatilities.\n",
        "periodic_returns = np.random.normal(drift/num_periods, volatility/np.sqrt(num_periods), num_periods) + 1\n",
        "price_change = initial_price * np.cumprod(periodic_returns)\n",
        "\n",
        "print(periodic_returns)\n",
        "print(price_change)\n",
        "\n",
        "plt.plot(list(range(num_periods)), price_change)\n",
        "plt.xlabel(\"Period\")  # add X-axis label\n",
        "plt.ylabel(\"Price Change\")  # add Y-axis label\n",
        "plt.title(\"GBM Simulation\")  # add title\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "Jj1Sl5k741Dq",
        "outputId": "ada97186-912f-465e-d26e-02a342f665be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "VAR at 95%:  16.44853626951476\n",
            "1 day VaR @ 95% confidence: 16.45\n",
            "2 day VaR @ 95% confidence: 23.26\n",
            "3 day VaR @ 95% confidence: 28.49\n",
            "4 day VaR @ 95% confidence: 32.9\n",
            "5 day VaR @ 95% confidence: 36.78\n",
            "6 day VaR @ 95% confidence: 40.29\n",
            "7 day VaR @ 95% confidence: 43.52\n",
            "8 day VaR @ 95% confidence: 46.52\n",
            "9 day VaR @ 95% confidence: 49.35\n",
            "10 day VaR @ 95% confidence: 52.01\n",
            "11 day VaR @ 95% confidence: 54.55\n",
            "12 day VaR @ 95% confidence: 56.98\n",
            "13 day VaR @ 95% confidence: 59.31\n",
            "14 day VaR @ 95% confidence: 61.54\n",
            "15 day VaR @ 95% confidence: 63.7\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Max portfolio loss (VaR) over 15-day period')"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Calculating Value at Risk (VaR)\n",
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "\n",
        "confidence_level = 0.05 #95%\n",
        "initial_investment = 2000\n",
        "mean_investment = 2000\n",
        "std_investment = 10\n",
        "\n",
        "\"\"\"Value at risk (VaR) is a measure of the risk of loss for investments. It estimates how much a set of investments might lose (with a given probability),\n",
        "given normal market conditions, in a set time period such as a day.\"\"\"\n",
        "cutoff = norm.ppf(confidence_level, mean_investment, std_investment)\n",
        "var_1d1 = initial_investment - cutoff\n",
        "print(\"VAR at 95%: \",var_1d1)\n",
        "\n",
        "var_array = []\n",
        "num_days = int(15)\n",
        "for x in range(1, num_days+1):\n",
        "    var_array.append(np.round(var_1d1 * np.sqrt(x),2))\n",
        "    print(str(x) + \" day VaR @ 95% confidence: \" + str(np.round(var_1d1 * np.sqrt(x),2)))\n",
        "\n",
        "# Build plot\n",
        "plt.plot(list(range(num_days)),var_array,\"r\")\n",
        "plt.xlabel(\"Day #\")\n",
        "plt.ylabel(\"Max portfolio loss (USD)\")\n",
        "plt.title(\"Max portfolio loss (VaR) over 15-day period\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "38exTvW7E-sG",
        "outputId": "509bacb9-97b1-4995-faba-38eaae815fb2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[101.90862688 102.77414628 100.39856901  99.73233411 102.53304961]\n"
          ]
        }
      ],
      "source": [
        "# Calculating Exponential Moving Average (EMA)\n",
        "import numpy as np\n",
        "\n",
        "price_series = np.array([100, 105, 102, 98, 100, 104, 107])\n",
        "period = 3\n",
        "\n",
        "weights = np.exp(np.linspace(-1., 0., period))\n",
        "weights /= np.sum(weights)\n",
        "\n",
        "# The exponential moving average (EMA) is a technical chart indicator that tracks the price of an investment (like a stock or commodity) over time.\n",
        "ema = np.convolve(price_series, weights, mode='valid')\n",
        "print(ema)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WdS2x4JbZ7-J",
        "outputId": "227f0b17-418c-4336-93a5-e9e35ecec7ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 80 110]\n"
          ]
        }
      ],
      "source": [
        "# Value Investment Strategy\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "prices = np.array([100, 90, 80, 120, 150, 110])\n",
        "book_values = np.array([80, 85, 90, 100, 120, 130])\n",
        "\n",
        "price_to_book_ratio = prices / book_values\n",
        "undervalued_stocks = price_to_book_ratio < 1\n",
        "\n",
        "selected_stocks = prices[undervalued_stocks]\n",
        "print(selected_stocks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nkl0YmYZO9dm"
      },
      "source": [
        "## Advance use cases of Numpy in Finance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSWGAjoFFhlZ",
        "outputId": "fe30b5c0-8c6c-49a4-e2cc-3e734ceb9fdb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.53974073 0.07552603 0.38473324]\n",
            "0.052336811718006464\n",
            "0.16408285049858687\n"
          ]
        }
      ],
      "source": [
        "# Portfolio Optimisation\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "\"\"\"\n",
        "Markowitz created a formula that allows an investor to mathematically trade off risk tolerance and reward expectations, resulting in the ideal portfolio.\n",
        "\n",
        "This theory was based on two main concepts:\n",
        "\n",
        "1. Every investor’s goal is to maximize return for any level of risk\n",
        "2. Risk can be reduced by diversifying a portfolio through individual, unrelated securities\n",
        "\"\"\"\n",
        "\n",
        "# Define expected returns, covariance matrix, and risk tolerance\n",
        "expected_returns = np.array([0.05, 0.03, 0.06])\n",
        "covariance_matrix = np.array([[0.04, 0.02, 0.01], [0.02, 0.06, 0.03], [0.01, 0.03, 0.05]])\n",
        "risk_tolerance = 0.05\n",
        "\n",
        "# Perform portfolio optimization\n",
        "num_assets = len(expected_returns)\n",
        "\n",
        "# Define the objective function to minimize portfolio risk\n",
        "def portfolio_risk(weights):\n",
        "    return np.sqrt(np.dot(weights.T, np.dot(covariance_matrix, weights)))\n",
        "\n",
        "# Define the constraint function for the sum of weights to be 1\n",
        "def constraint_sum_of_weights(weights):\n",
        "    return np.sum(weights) - 1.0\n",
        "\n",
        "# Use scipy.optimize.minimize to find the minimum-risk portfolio\n",
        "from scipy.optimize import minimize\n",
        "\n",
        "# Set the initial weights to be equal weights\n",
        "initial_weights = np.array([1 / num_assets] * num_assets)\n",
        "\n",
        "# Define the bounds for each weight (0 to 1)\n",
        "bounds = [(0, 1)] * num_assets\n",
        "\n",
        "# Define the constraint dict for the sum of weights\n",
        "constraint = {'type': 'eq', 'fun': constraint_sum_of_weights}\n",
        "\n",
        "# Minimize the portfolio risk under the given constraints\n",
        "result = minimize(portfolio_risk, initial_weights, method='SLSQP', bounds=bounds, constraints=constraint)\n",
        "\n",
        "# Extract the optimized weights and calculate the portfolio return and risk\n",
        "weights = result.x\n",
        "portfolio_return = np.dot(weights, expected_returns)\n",
        "portfolio_risk = np.sqrt(np.dot(weights.T, np.dot(covariance_matrix, weights)))\n",
        "\n",
        "print(weights)\n",
        "print(portfolio_return)\n",
        "print(portfolio_risk)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rKnOY15dFN2t",
        "outputId": "5d7e158a-e5b6-4ea3-ef31-ce9d83b9de8f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8.021352235143176\n",
            "7.9004418077181455\n"
          ]
        }
      ],
      "source": [
        "# Black-Scholes Option Pricing Model\n",
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "\n",
        "\"\"\"\n",
        "This model is used to find the current value of a call option whose ultimate value depends on the price of the stock at the expiration date.\n",
        "Because the stock price keeps changing, the value of this call option will change too\n",
        "\"\"\"\n",
        "\n",
        "\"\"\"\n",
        "This function calculates the call and put option prices using the Black-Scholes formula. It takes the following inputs:\n",
        "\n",
        "S: Current stock price\n",
        "K: Strike price of the option\n",
        "r: Risk-free interest rate\n",
        "sigma: Volatility of the underlying asset\n",
        "T: Time to maturity of the option\n",
        "The function computes d1 and d2, which are intermediate variables used in the Black-Scholes formula.\n",
        "Then, it calculates the call and put option prices using the cumulative distribution function (CDF) of the standard normal distribution.\n",
        "\"\"\"\n",
        "\n",
        "def black_scholes(S, K, r, sigma, T):\n",
        "    d1 = (np.log(S / K) + (r + 0.5 * sigma**2) * T) / (sigma * np.sqrt(T))\n",
        "    d2 = d1 - sigma * np.sqrt(T)\n",
        "    call_price = S * norm.cdf(d1) - K * np.exp(-r * T) * norm.cdf(d2)\n",
        "    put_price = K * np.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)\n",
        "    return call_price, put_price\n",
        "\n",
        "stock_price = 100.0\n",
        "strike_price = 105.0\n",
        "risk_free_rate = 0.05\n",
        "volatility = 0.2\n",
        "time_to_maturity = 1.0\n",
        "\n",
        "call_option_price, put_option_price = black_scholes(stock_price, strike_price, risk_free_rate, volatility, time_to_maturity)\n",
        "print(call_option_price) # Which Price to Buy\n",
        "print(put_option_price) # Which Price to Sell"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "bKh8LYQhPIxk",
        "outputId": "18643600-6ec1-45ed-f730-753b578bdba3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Monte Carlo Simulation for Risk Analysis')"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Monte Carlo Simulation for Risk Analysis\n",
        "\n",
        "\"\"\"\n",
        "Monte Carlo simulation performs risk analysis by building models of possible results\n",
        "by substituting a range of values—called a probability distribution—for any factor that has inherent uncertainty.\n",
        "\"\"\"\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "initial_price = 100.0\n",
        "drift = 0.05\n",
        "volatility = 0.2\n",
        "time_horizon = 1.0\n",
        "num_simulations = 10\n",
        "num_periods = 24\n",
        "\n",
        "daily_returns = np.random.normal(drift/num_periods, volatility/np.sqrt(num_periods), (num_periods, num_simulations)) + 1\n",
        "price_series = initial_price * np.cumprod(daily_returns, axis=0)\n",
        "\n",
        "final_prices = price_series[-1]\n",
        "mean_price = np.mean(final_prices)\n",
        "std_deviation = np.std(final_prices)\n",
        "\n",
        "plt.plot(final_prices)\n",
        "plt.xlabel(\"Simulations\")\n",
        "plt.ylabel(\"Final Prices\")\n",
        "plt.title(\"Monte Carlo Simulation for Risk Analysis\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "396Q5yp_a4S0"
      },
      "source": [
        "## Pandas in Finance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQaXlENiQVZz",
        "outputId": "da62ef9a-4c51-4911-ebd4-b1311a3ff9bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sector\n",
            "AI              105.800000\n",
            "Data Science     95.300000\n",
            "Energy          110.100000\n",
            "Finance          63.783333\n",
            "IT              103.850000\n",
            "Retail           85.600000\n",
            "Name: Price, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Assuming we have a DataFrame with stock data\n",
        "# Dummy data\n",
        "data = ['2023-05-21', '2023-05-22', '2023-05-23', '2023-05-23', '2023-05-23',\n",
        "        '2023-05-25', '2023-05-26', '2023-05-26', '2023-05-27', '2023-05-30']\n",
        "stock_names = ['ABC', 'XYZ', 'DEF', 'GHI', 'JKL', 'MNO', 'PQR', 'STU', 'VWX', 'YZA']\n",
        "sector = ['IT','Finance','IT','Finance','IT','Energy','Retail','Data Science','AI','Finance']\n",
        "prices = [100.50, 75.20, 120.80, 50.75, 90.25, 110.10, 85.60, 95.30, 105.80, 65.40]\n",
        "\n",
        "# Create dataframe\n",
        "df = pd.DataFrame({'Date': data, 'Stock Names': stock_names, 'Price': prices, 'Sector':sector})\n",
        "\n",
        "# Grouping data by sector and calculating average price\n",
        "avg_price_by_sector = df.groupby('Sector')['Price'].mean()\n",
        "print(avg_price_by_sector)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lujlcAoLbtoE",
        "outputId": "02a3e8a2-e6b8-43ab-acff-5d8246748040"
      },
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        {
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              "      <th>Date</th>\n",
              "      <th>Stock Names</th>\n",
              "      <th>Price</th>\n",
              "      <th>Sector</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2023-05-21</td>\n",
              "      <td>ABC</td>\n",
              "      <td>100.50</td>\n",
              "      <td>IT</td>\n",
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              "      <th>1</th>\n",
              "      <td>2023-05-22</td>\n",
              "      <td>XYZ</td>\n",
              "      <td>75.20</td>\n",
              "      <td>Finance</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>GHI</td>\n",
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              "    <tr>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "        Date Stock Names   Price   Sector\n",
              "0 2023-05-21         ABC  100.50       IT\n",
              "1 2023-05-22         XYZ   75.20  Finance\n",
              "2 2023-05-23         DEF  120.80       IT\n",
              "3 2023-05-23         GHI   50.75  Finance\n",
              "4 2023-05-23         JKL   90.25       IT"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Data Cleaning and Transformation\n",
        "# Removing rows with missing values\n",
        "df.dropna(inplace=True) # Watch Video Link in Description\n",
        "\n",
        "# Converting date column to datetime format\n",
        "df['Date'] = pd.to_datetime(df['Date']) # Watch Vidoe Link in Description\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yR4rut1zb_78",
        "outputId": "4b35ad02-ec63-4706-f8f5-e834a7f2057c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Date\n",
            "2023-05-31    89.97\n",
            "Freq: M, Name: Price, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Time Series Resampling and Aggregation\n",
        "# Converting date column to datetime format\n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "\n",
        "# Resampling to monthly data and calculating average price\n",
        "monthly_avg_price = df.resample('M', on='Date')['Price'].mean()\n",
        "print(monthly_avg_price)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "RM2pvAVrcGNk",
        "outputId": "7360cc57-dc40-4025-cfa6-b19b5d487b16"
      },
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        {
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              "      <td>2023-05-22</td>\n",
              "      <td>XYZ</td>\n",
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              "      <td>87.850</td>\n",
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              "      <td>GHI</td>\n",
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              "      <td>85.775</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
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              "      <td>JKL</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2023-05-25</td>\n",
              "      <td>MNO</td>\n",
              "      <td>110.10</td>\n",
              "      <td>Energy</td>\n",
              "      <td>100.175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2023-05-26</td>\n",
              "      <td>PQR</td>\n",
              "      <td>85.60</td>\n",
              "      <td>Retail</td>\n",
              "      <td>97.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2023-05-26</td>\n",
              "      <td>STU</td>\n",
              "      <td>95.30</td>\n",
              "      <td>Data Science</td>\n",
              "      <td>90.450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2023-05-27</td>\n",
              "      <td>VWX</td>\n",
              "      <td>105.80</td>\n",
              "      <td>AI</td>\n",
              "      <td>100.550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2023-05-30</td>\n",
              "      <td>YZA</td>\n",
              "      <td>65.40</td>\n",
              "      <td>Finance</td>\n",
              "      <td>85.600</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Date Stock Names   Price        Sector  RollingAvg\n",
              "0 2023-05-21         ABC  100.50            IT         NaN\n",
              "1 2023-05-22         XYZ   75.20       Finance      87.850\n",
              "2 2023-05-23         DEF  120.80            IT      98.000\n",
              "3 2023-05-23         GHI   50.75       Finance      85.775\n",
              "4 2023-05-23         JKL   90.25            IT      70.500\n",
              "5 2023-05-25         MNO  110.10        Energy     100.175\n",
              "6 2023-05-26         PQR   85.60        Retail      97.850\n",
              "7 2023-05-26         STU   95.30  Data Science      90.450\n",
              "8 2023-05-27         VWX  105.80            AI     100.550\n",
              "9 2023-05-30         YZA   65.40       Finance      85.600"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Rolling Window Analysis\n",
        "# Calculating 2-day rolling average\n",
        "df['RollingAvg'] = df['Price'].rolling(window=2).mean()\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "outputId": "82de4126-bb5e-4178-b8d4-32eedd29961d"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Data Visualization\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "# Plotting line chart of stock prices\n",
        "plt.plot(df['Date'], df['Price'])\n",
        "plt.title('Stock Prices Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJzpwl__cbbD",
        "outputId": "e877975c-2d93-47e0-872a-d60831226eed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Month        April  February  January   March\n",
            "Category                                     \n",
            "Clothing     468.0       NaN      NaN     NaN\n",
            "Electronics  985.0     802.0   1251.0   191.0\n",
            "Home Decor     NaN       NaN      NaN  1804.0\n"
          ]
        }
      ],
      "source": [
        "# Pivot Tables\n",
        "sales = pd.DataFrame({\n",
        "    'Category': np.random.choice(['Electronics', 'Clothing', 'Home Decor'], size=10),\n",
        "    'Month': np.random.choice(['January', 'February', 'March', 'April'], size=10),\n",
        "    'Sales': np.random.randint(100, 1000, size=10)\n",
        "})\n",
        "# Creating a pivot table to summarize sales by product category\n",
        "pivot_table = sales.pivot_table(values='Sales', index='Category', columns='Month', aggfunc='sum')\n",
        "print(pivot_table)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g6cUYLOShSmy",
        "outputId": "f87eb08a-b3e5-48fd-e772-a441226cc115"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "         Date Stock Names  Price  Sector\n",
            "0  2023-05-21         ABC  100.5      IT\n",
            "2  2023-05-23         DEF  120.8      IT\n",
            "5  2023-05-25         MNO  110.1  Energy\n",
            "8  2023-05-27         VWX  105.8      AI\n"
          ]
        }
      ],
      "source": [
        "# Data Filtering and Selection\n",
        "# Filtering stocks with price above 100\n",
        "high_priced_stocks = df[df['Price'] > 100]\n",
        "print(high_priced_stocks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "2XPgLmPKhcnR",
        "outputId": "5092ac70-09f2-47ab-fa0d-f1177b72f860"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0592a7b9-b7f0-443c-a844-d9a5d03678b5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Stock Names</th>\n",
              "      <th>Price</th>\n",
              "      <th>Sector</th>\n",
              "      <th>Return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-05-21</td>\n",
              "      <td>ABC</td>\n",
              "      <td>100.50</td>\n",
              "      <td>IT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-05-22</td>\n",
              "      <td>XYZ</td>\n",
              "      <td>75.20</td>\n",
              "      <td>Finance</td>\n",
              "      <td>-0.251741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-05-23</td>\n",
              "      <td>DEF</td>\n",
              "      <td>120.80</td>\n",
              "      <td>IT</td>\n",
              "      <td>0.606383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-05-23</td>\n",
              "      <td>GHI</td>\n",
              "      <td>50.75</td>\n",
              "      <td>Finance</td>\n",
              "      <td>-0.579884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-05-23</td>\n",
              "      <td>JKL</td>\n",
              "      <td>90.25</td>\n",
              "      <td>IT</td>\n",
              "      <td>0.778325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2023-05-25</td>\n",
              "      <td>MNO</td>\n",
              "      <td>110.10</td>\n",
              "      <td>Energy</td>\n",
              "      <td>0.219945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2023-05-26</td>\n",
              "      <td>PQR</td>\n",
              "      <td>85.60</td>\n",
              "      <td>Retail</td>\n",
              "      <td>-0.222525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2023-05-26</td>\n",
              "      <td>STU</td>\n",
              "      <td>95.30</td>\n",
              "      <td>Data Science</td>\n",
              "      <td>0.113318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2023-05-27</td>\n",
              "      <td>VWX</td>\n",
              "      <td>105.80</td>\n",
              "      <td>AI</td>\n",
              "      <td>0.110178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2023-05-30</td>\n",
              "      <td>YZA</td>\n",
              "      <td>65.40</td>\n",
              "      <td>Finance</td>\n",
              "      <td>-0.381853</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0592a7b9-b7f0-443c-a844-d9a5d03678b5')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
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            "text/plain": [
              "         Date Stock Names   Price        Sector    Return\n",
              "0  2023-05-21         ABC  100.50            IT       NaN\n",
              "1  2023-05-22         XYZ   75.20       Finance -0.251741\n",
              "2  2023-05-23         DEF  120.80            IT  0.606383\n",
              "3  2023-05-23         GHI   50.75       Finance -0.579884\n",
              "4  2023-05-23         JKL   90.25            IT  0.778325\n",
              "5  2023-05-25         MNO  110.10        Energy  0.219945\n",
              "6  2023-05-26         PQR   85.60        Retail -0.222525\n",
              "7  2023-05-26         STU   95.30  Data Science  0.113318\n",
              "8  2023-05-27         VWX  105.80            AI  0.110178\n",
              "9  2023-05-30         YZA   65.40       Finance -0.381853"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Calculating daily returns\n",
        "df['Return'] = df['Price'].pct_change()\n",
        "df"
      ]
    },
    {
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      "execution_count": null,
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        "id": "cXHmqiRuh1RB",
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              "                       Date Stock Names   Price        Sector    Return\n",
              "0 2023-05-20 20:00:00-04:00         ABC  100.50            IT       NaN\n",
              "1 2023-05-21 20:00:00-04:00         XYZ   75.20       Finance -0.251741\n",
              "2 2023-05-22 20:00:00-04:00         DEF  120.80            IT  0.606383\n",
              "3 2023-05-22 20:00:00-04:00         GHI   50.75       Finance -0.579884\n",
              "4 2023-05-22 20:00:00-04:00         JKL   90.25            IT  0.778325\n",
              "5 2023-05-24 20:00:00-04:00         MNO  110.10        Energy  0.219945\n",
              "6 2023-05-25 20:00:00-04:00         PQR   85.60        Retail -0.222525\n",
              "7 2023-05-25 20:00:00-04:00         STU   95.30  Data Science  0.113318\n",
              "8 2023-05-26 20:00:00-04:00         VWX  105.80            AI  0.110178\n",
              "9 2023-05-29 20:00:00-04:00         YZA   65.40       Finance -0.381853"
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          "execution_count": 23,
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      "source": [
        "# Handling Time Zones\n",
        "# Converting date column to datetime format and setting time zone\n",
        "df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize('UTC').dt.tz_convert('US/Eastern')\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypyW46fmiQwK"
      },
      "source": [
        "## Pandas Advance"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "F5yTXTAHiCk5",
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        {
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              "      <th>7</th>\n",
              "      <td>2023-05-25 20:00:00-04:00</td>\n",
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              "  "
            ],
            "text/plain": [
              "                       Date Stock Names   Price        Sector    Return  \\\n",
              "0 2023-05-20 20:00:00-04:00         ABC  100.50            IT       NaN   \n",
              "1 2023-05-21 20:00:00-04:00         XYZ   75.20       Finance -0.251741   \n",
              "2 2023-05-22 20:00:00-04:00         DEF  120.80            IT  0.606383   \n",
              "3 2023-05-22 20:00:00-04:00         GHI   50.75       Finance -0.579884   \n",
              "4 2023-05-22 20:00:00-04:00         JKL   90.25            IT  0.778325   \n",
              "5 2023-05-24 20:00:00-04:00         MNO  110.10        Energy  0.219945   \n",
              "6 2023-05-25 20:00:00-04:00         PQR   85.60        Retail -0.222525   \n",
              "7 2023-05-25 20:00:00-04:00         STU   95.30  Data Science  0.113318   \n",
              "8 2023-05-26 20:00:00-04:00         VWX  105.80            AI  0.110178   \n",
              "9 2023-05-29 20:00:00-04:00         YZA   65.40       Finance -0.381853   \n",
              "\n",
              "   50-day MA  200-day MA  \n",
              "0        NaN         NaN  \n",
              "1        NaN         NaN  \n",
              "2  98.833333         NaN  \n",
              "3  82.250000         NaN  \n",
              "4  87.266667       87.50  \n",
              "5  83.700000       89.42  \n",
              "6  95.316667       91.50  \n",
              "7  97.000000       86.40  \n",
              "8  95.566667       97.41  \n",
              "9  88.833333       92.44  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Calculating 50-day and 200-day moving averages\n",
        "df['50-day MA'] = df['Price'].rolling(window=3).mean()\n",
        "df['200-day MA'] = df['Price'].rolling(window=5).mean()\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "xhtSQSEHiX_3",
        "outputId": "e571ca07-562a-4f49-82c2-cd69ac7f3adb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.8128911138923653, 1.3929912390488108, 1.2171464330413015, 0.30725907384230267, -0.3110137672090113, 0.5400500625782227, 1.2302878598247808, 0.49123904881101366, 0.031914893617021045]\n",
            "1.2071192205190289\n"
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              "      <th>Portfolio Value</th>\n",
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              "      <th>6</th>\n",
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              "      <td>3564</td>\n",
              "      <td>0.448192</td>\n",
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              "      <th>8</th>\n",
              "      <td>2022-01-09</td>\n",
              "      <td>2383</td>\n",
              "      <td>-0.331369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2022-01-10</td>\n",
              "      <td>1649</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "        Date  Portfolio Value    Return\n",
              "0 2022-01-01             1598       NaN\n",
              "1 2022-01-02             2897  0.812891\n",
              "2 2022-01-03             3824  0.319986\n",
              "3 2022-01-04             3543 -0.073483\n",
              "4 2022-01-05             2089 -0.410387\n",
              "5 2022-01-06             1101 -0.472954\n",
              "6 2022-01-07             2461  1.235241\n",
              "7 2022-01-08             3564  0.448192\n",
              "8 2022-01-09             2383 -0.331369\n",
              "9 2022-01-10             1649 -0.308015"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Portfolio Performance Analysis\n",
        "dates = pd.date_range(start='2022-01-01', periods=10, freq='D')\n",
        "portfolio_value = np.random.randint(1000, 5000, size=10)\n",
        "\n",
        "# Create dataframe\n",
        "df = pd.DataFrame({'Date': dates, 'Portfolio Value': portfolio_value})\n",
        "\n",
        "# Calculating portfolio returns\n",
        "df['Return'] = df['Portfolio Value'].pct_change()\n",
        "\n",
        "# Calculating portfolio performance metrics\n",
        "cumulative_return = (1 + df['Return'].iloc[1:]).cumprod() - 1\n",
        "print(list(cumulative_return))\n",
        "annualized_return = (1 + list(cumulative_return)[-1]) ** (252/len(df)) - 1\n",
        "print(annualized_return)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
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          "height": 362
        },
        "id": "OI9boYPPjc7J",
        "outputId": "b88e694e-729d-467a-ef01-1ef1ff6414ea"
      },
      "outputs": [
        {
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              "      <th>Stock Return</th>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                   Event  Stock Return  Market Return  Abnormal Return\n",
              "0  Earnings Announcement     -0.002127      -0.028544         0.026417\n",
              "1  Earnings Announcement      0.021370      -0.024896         0.046266\n",
              "2  Earnings Announcement      0.015162      -0.006480         0.021642\n",
              "3  Earnings Announcement      0.045195       0.008797         0.036397\n",
              "4  Earnings Announcement      0.048886      -0.014638         0.063524\n",
              "5  Earnings Announcement      0.007540       0.003330         0.004211\n",
              "6  Earnings Announcement     -0.014814      -0.027782         0.012969\n",
              "7  Earnings Announcement     -0.012066      -0.021378         0.009313\n",
              "8  Earnings Announcement     -0.022036      -0.007873        -0.014163\n",
              "9  Earnings Announcement      0.036122       0.020976         0.015146"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Event Studies\n",
        "# Create dummy DataFrame with stock and event data\n",
        "df = pd.DataFrame({\n",
        "    'Event': ['Earnings Announcement'] * 10,\n",
        "    'Stock Return': np.random.uniform(-0.05, 0.05, 10),\n",
        "    'Market Return': np.random.uniform(-0.03, 0.03, 10)\n",
        "})\n",
        "\n",
        "# Filtering data for a specific event\n",
        "event_df = df[df['Event'] == 'Earnings Announcement']\n",
        "\n",
        "# Calculating abnormal returns around the event\n",
        "event_df['Abnormal Return'] = event_df['Stock Return'] - event_df['Market Return']\n",
        "event_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vda-cUbHjzmj",
        "outputId": "8bbe5968-e9f9-4e76-d46e-38f3cd711354"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.02411869224362329\n",
            "-0.018786004559938833\n"
          ]
        }
      ],
      "source": [
        "# Risk Analysis and VaR Calculation\n",
        "# Calculating portfolio volatility\n",
        "volatility = np.std(df['Stock Return'])\n",
        "\n",
        "# Calculating Value at Risk (VaR) at a specific confidence level\n",
        "confidence_level = 0.95\n",
        "var = np.percentile(df['Stock Return'], 100 - confidence_level * 100)\n",
        "print(volatility)\n",
        "print(var)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "coUyPz34mIfd",
        "outputId": "81c73720-4d64-4cec-f8ea-cb059ab60037"
      },
      "outputs": [
        {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Net Income</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>Shareholders Equity</th>\n",
              "      <th>Profit Margin</th>\n",
              "      <th>Return on Equity</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "   Net Income  Revenue  Shareholders Equity  Profit Margin  Return on Equity\n",
              "0        1202    16629                18819       0.072283          0.063872\n",
              "1        9463     9077                25131       1.042525          0.376547\n",
              "2        9462     9144                34472       1.034777          0.274484\n",
              "3        2851     7943                20420       0.358932          0.139618\n",
              "4        7879     9564                24234       0.823818          0.325122"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Fundamental Analysis\n",
        "import random\n",
        "num_rows = 10\n",
        "df = pd.DataFrame()  # Limit the DataFrame to the desired number of rows\n",
        "\n",
        "# Generate random values for Net Income, Revenue, and Shareholders Equity\n",
        "df['Net Income'] = [random.randint(1000, 10000) for _ in range(num_rows)]\n",
        "df['Revenue'] = [random.randint(5000, 20000) for _ in range(num_rows)]\n",
        "df['Shareholders Equity'] = [random.randint(10000, 50000) for _ in range(num_rows)]\n",
        "\n",
        "\n",
        "# Calculating financial ratios\n",
        "df['Profit Margin'] = df['Net Income'] / df['Revenue']\n",
        "df['Return on Equity'] = df['Net Income'] / df['Shareholders Equity']\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "simq0JgaR5EG",
        "outputId": "ee51fc1b-15b2-42fc-cce9-17407e7006f7"
      },
      "outputs": [
        {
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "\n",
        "# Generate random data following a normal distribution\n",
        "data = np.random.normal(loc=0, scale=1, size=1000)\n",
        "\n",
        "# Create a histogram using Plotly\n",
        "fig = go.Figure(data=[go.Histogram(x=data)])\n",
        "fig.show()\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rUAgquqXxnG",
        "outputId": "894bc6de-27b2-40c9-9838-90b292eed9de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Training Set:\n",
            "24    16\n",
            "12     5\n",
            "0     15\n",
            "4      7\n",
            "16     6\n",
            "5      6\n",
            "13    21\n",
            "11     9\n",
            "22     4\n",
            "1     11\n",
            "2      7\n",
            "25     9\n",
            "3     13\n",
            "21    17\n",
            "26     4\n",
            "18     9\n",
            "30     7\n",
            "20    21\n",
            "7     10\n",
            "10     6\n",
            "14    17\n",
            "28    20\n",
            "19    22\n",
            "6     12\n",
            "Name: Complaints, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "# Generate dummy data\n",
        "np.random.seed(42)\n",
        "dates = pd.date_range(start='2022-01-01', end='2022-01-31', freq='D')\n",
        "complaints = np.random.negative_binomial(n=5, p=0.3, size=len(dates))\n",
        "\n",
        "# Create a DataFrame\n",
        "data = pd.DataFrame({'Date': dates, 'Day': dates.day, 'Weekday': dates.weekday, 'Complaints': complaints})\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(data[['Day', 'Weekday']], data['Complaints'], test_size=0.2, random_state=42)\n",
        "\n",
        "# Print the training set\n",
        "print(\"\\nTraining Set:\")\n",
        "print(y_train)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "XNdJX_bDSLt5",
        "outputId": "8235a349-596c-46ed-92f9-57adaebdb090"
      },
      "outputs": [
        {
          "data": {
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          "metadata": {},
          "output_type": "display_data"
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      ],
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "\n",
        "# Generate random data following a Poisson distribution\n",
        "data = np.random.poisson(lam=5, size=1000)\n",
        "\n",
        "# Create a histogram using Plotly\n",
        "fig = go.Figure(data=[go.Histogram(x=data)])\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "CHIJnWjISRsU",
        "outputId": "d595300b-34d6-433b-e2c5-f5d28872fde9"
      },
      "outputs": [
        {
          "data": {
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "from scipy.stats import nbinom\n",
        "\n",
        "# Generate random data following a negative binomial distribution\n",
        "data = nbinom.rvs(n=5, p=0.5, size=1000)\n",
        "\n",
        "# Create a histogram using Plotly\n",
        "fig = go.Figure(data=[go.Histogram(x=data)])\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "UuzPangkSRxo",
        "outputId": "f0d155dc-5392-4685-c033-71903e89bf8c"
      },
      "outputs": [
        {
          "data": {
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "from scipy.stats import binom\n",
        "\n",
        "# Generate random data following a binomial distribution\n",
        "data = binom.rvs(n=1, p=0.3, size=1000)\n",
        "\n",
        "# Create a histogram using Plotly\n",
        "fig = go.Figure(data=[go.Histogram(x=data)])\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "GSHX-r56SR03",
        "outputId": "3a953606-8206-41e7-fa13-a37e17341caf"
      },
      "outputs": [
        {
          "data": {
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              "</body>\n",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "from scipy.stats import gamma\n",
        "\n",
        "# Generate random data following a gamma distribution\n",
        "data = gamma.rvs(a=5, scale=2, size=1000)\n",
        "\n",
        "# Create a histogram using Plotly\n",
        "fig = go.Figure(data=[go.Histogram(x=data)])\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6tZujQPSdpQ"
      },
      "source": [
        "# **Week 2 : Financial Data Analysis Course**\n",
        "\n",
        "## Agenda\n",
        "### 📍 Missing Data and Outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "425YAVAYTgK5"
      },
      "source": [
        "### Missing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L4mC8VwMScIf",
        "outputId": "43cf0877-62c1-4f48-b304-5f6e7d324fbd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A    4\n",
            "B    3\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Identifying Missing Data\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Create dummy data\n",
        "data = pd.DataFrame({'A': [1, 2, np.nan,31,np.nan,5,9,10,33,np.nan,54,np.nan, 4, 5],\n",
        "                     'B': [9, 1.2,5,51,np.nan,35,59,10,13,np.nan,14,np.nan, 8, 5.3]})\n",
        "# Check for missing values\n",
        "missing_values = data.isnull().sum()\n",
        "print(missing_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWajcjufS5J0",
        "outputId": "cf024145-1fbd-4122-cd92-33212522488d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-------------Before Imputation------------------\n",
            "    Age   Salary Education      City\n",
            "0  25.0  50000.0  Bachelor  New York\n",
            "1  30.0  60000.0    Master   Chicago\n",
            "2   NaN  70000.0       PhD   Chicago\n",
            "3  35.0      NaN  Bachelor  New York\n",
            "4  40.0  80000.0       NaN    Boston\n",
            "5   NaN  90000.0    Master    Boston\n",
            "6  45.0      NaN       PhD       NaN\n",
            "-------------After Imputation------------------\n",
            "    Age   Salary Education      City\n",
            "0  25.0  50000.0  Bachelor  New York\n",
            "1  30.0  60000.0    Master   Chicago\n",
            "2  35.0  70000.0       PhD   Chicago\n",
            "3  35.0  70000.0  Bachelor  New York\n",
            "4  40.0  80000.0  Bachelor    Boston\n",
            "5  35.0  90000.0    Master    Boston\n",
            "6  45.0  70000.0       PhD    Boston\n"
          ]
        }
      ],
      "source": [
        "# Handling Missing Data\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Create dummy data\n",
        "data = pd.DataFrame({\n",
        "    'Age': [25, 30, np.nan, 35, 40, np.nan, 45],\n",
        "    'Salary': [50000, 60000, 70000, np.nan, 80000, 90000, np.nan],\n",
        "    'Education': ['Bachelor', 'Master', 'PhD', 'Bachelor', np.nan, 'Master', 'PhD'],\n",
        "    'City': ['New York', 'Chicago', 'Chicago', 'New York', 'Boston', 'Boston', np.nan]\n",
        "})\n",
        "\n",
        "print(\"-------------Before Imputation------------------\")\n",
        "print(data)\n",
        "\n",
        "# Fill missing values with mean for numerical data\n",
        "data['Age'].fillna(data['Age'].mean(), inplace=True)\n",
        "data['Salary'].fillna(data['Salary'].mean(), inplace=True)\n",
        "\n",
        "# Fill missing values with median for numerical data\n",
        "data['Age'].fillna(data['Age'].median(), inplace=True)\n",
        "data['Salary'].fillna(data['Salary'].median(), inplace=True)\n",
        "\n",
        "# Fill missing values with mode for categorical data\n",
        "data['Education'].fillna(data['Education'].mode()[0], inplace=True)\n",
        "data['City'].fillna(data['City'].mode()[0], inplace=True)\n",
        "\n",
        "print(\"-------------After Imputation------------------\")\n",
        "print(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "lSUinS3vTsnz",
        "outputId": "34a87fe6-9aa7-4acd-8da8-2069a348b09b"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>Age</th>\n",
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              "      <td>120</td>\n",
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              "      <td>45</td>\n",
              "      <td>90000</td>\n",
              "      <td>Master</td>\n",
              "      <td>Boston</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>50</td>\n",
              "      <td>100000</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Boston</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>55</td>\n",
              "      <td>150000</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>60</td>\n",
              "      <td>200000</td>\n",
              "      <td>Master</td>\n",
              "      <td>Chicago</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>65</td>\n",
              "      <td>250000</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Chicago</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>70</td>\n",
              "      <td>300000</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>75</td>\n",
              "      <td>350000</td>\n",
              "      <td>Master</td>\n",
              "      <td>Boston</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>80</td>\n",
              "      <td>400000</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Boston</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>85</td>\n",
              "      <td>1000000</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>New York</td>\n",
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            "text/plain": [
              "    Age   Salary Education      City\n",
              "0   120    50000  Bachelor  New York\n",
              "1    30    60000    Master   Chicago\n",
              "2    35     5000       PhD   Chicago\n",
              "3    40    80000  Bachelor  New York\n",
              "4    45    90000    Master    Boston\n",
              "5    50   100000       PhD    Boston\n",
              "6    55   150000  Bachelor  New York\n",
              "7    60   200000    Master   Chicago\n",
              "8    65   250000       PhD   Chicago\n",
              "9    70   300000  Bachelor  New York\n",
              "10   75   350000    Master    Boston\n",
              "11   80   400000       PhD    Boston\n",
              "12   85  1000000  Bachelor  New York"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Detecting Outliers\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Create dummy data with outliers\n",
        "data = pd.DataFrame({\n",
        "    'Age': [25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85],\n",
        "    'Salary': [50000, 60000, 70000, 80000, 90000, 100000, 150000, 200000, 250000, 300000, 350000, 400000, 1000000],\n",
        "    'Education': ['Bachelor', 'Master', 'PhD', 'Bachelor', 'Master', 'PhD', 'Bachelor', 'Master', 'PhD', 'Bachelor', 'Master', 'PhD', 'Bachelor'],\n",
        "    'City': ['New York', 'Chicago', 'Chicago', 'New York', 'Boston', 'Boston', 'New York', 'Chicago', 'Chicago', 'New York', 'Boston', 'Boston', 'New York']\n",
        "})\n",
        "\n",
        "# Add outliers to the data\n",
        "data.loc[0, 'Age'] = 120\n",
        "data.loc[2, 'Salary'] = 5000\n",
        "\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yPGPamo7Vr7S",
        "outputId": "dc05ffb3-f16d-49b9-e6e4-de0666d8558a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Outliers:\n",
            "    Age   Salary Education      City\n",
            "0   120    50000  Bachelor  New York\n",
            "12   85  1000000  Bachelor  New York\n"
          ]
        }
      ],
      "source": [
        "# Detect outliers using z-score\n",
        "z_scores = (data[['Age', 'Salary']].apply(lambda x: (x - x.mean()) / x.std())).abs()\n",
        "outliers = data[(z_scores > 2).any(axis=1)] # z_score > 2 means, data is away from 95% of the data because data is 2 Standard deviation away from mean.\n",
        "\n",
        "# Print outliers\n",
        "print(\"Outliers:\")\n",
        "print(outliers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "0n_Es3TfVyGe",
        "outputId": "3760f729-6b12-4296-a7fd-75209cf800d9"
      },
      "outputs": [
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      "source": [
        "# Plot outliers using Plotly\n",
        "fig = go.Figure()\n",
        "\n",
        "# Scatter plot for Age column\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=data.index,\n",
        "    y=data['Age'],\n",
        "    mode='markers',\n",
        "    name='Age'\n",
        "))\n",
        "\n",
        "# Scatter plot for Salary column\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=data.index,\n",
        "    y=data['Salary'],\n",
        "    mode='markers',\n",
        "    name='Salary'\n",
        "))\n",
        "\n",
        "# Scatter plot for outliers\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=outliers.index,\n",
        "    y=outliers['Age'],\n",
        "    mode='markers',\n",
        "    name='Outliers',\n",
        "    marker=dict(color='red', size=20)\n",
        "))\n",
        "\n",
        "fig.update_layout(title='Outlier Detection',\n",
        "                  xaxis_title='Index',\n",
        "                  yaxis_title='Value')\n",
        "\n",
        "fig.show()"
      ]
    },
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        "id": "jDxMbMTIWWyG",
        "outputId": "259648e5-dc46-46c8-ac4e-f0e4472a9767"
      },
      "outputs": [
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          "metadata": {},
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      ],
      "source": [
        "# Handling Outliers\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Dummy data with outliers\n",
        "data = pd.DataFrame({\n",
        "    'Age': [25, 30, 35, 120, 45, 50, 55, 60, 65, 70, 75, 80, 85],\n",
        "    'Salary': [50000, 60000, 70000, 5000, 90000, 100000, 150000, 200000, 250000, 300000, 350000, 400000, 1000000]\n",
        "})\n",
        "\n",
        "\n",
        "# Create a scatter plot\n",
        "fig = go.Figure(data=go.Scatter(x=data['Age'], y=data['Salary'], mode='markers'))\n",
        "\n",
        "# Update marker settings\n",
        "fig.update_traces(marker=dict(symbol='square', size=10))\n",
        "\n",
        "# Add axis labels and title\n",
        "fig.update_layout(\n",
        "    xaxis_title='Age',\n",
        "    yaxis_title='Salary',\n",
        "    title='Outlier Handling'\n",
        ")\n",
        "\n",
        "# Show the plot\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "p2MraivyXAOd",
        "outputId": "718a667e-bf99-4936-8e2b-cabe4a812100"
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      "outputs": [
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          "text": [
            "29.68\n",
            "87.23999999999997\n"
          ]
        },
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          "metadata": {},
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      "source": [
        "# Method 1: Winsorization\n",
        "\"\"\"\n",
        "Winsorizing or winsorization is the transformation of statistics by limiting extreme values in the statistical\n",
        "data to reduce the effect of possibly spurious outliers\n",
        "\"\"\"\n",
        "\n",
        "def winsorize(data, col, q):\n",
        "    lower = data[col].quantile(q)\n",
        "    print(lower)\n",
        "    upper = data[col].quantile(1 - q)\n",
        "    print(upper)\n",
        "    data[col] = np.where(data[col] < lower, lower, np.where(data[col] > upper, upper, data[col]))\n",
        "    return data\n",
        "\n",
        "# Apply winsorization to Age column\n",
        "mod_data = winsorize(data, 'Age', 0.05)\n",
        "\n",
        "# Create a scatter plot\n",
        "fig = go.Figure(data=go.Scatter(x=mod_data['Age'], y=mod_data['Salary'], mode='markers'))\n",
        "\n",
        "# Update marker settings\n",
        "fig.update_traces(marker=dict(symbol='square', size=10))\n",
        "\n",
        "# Add axis labels and title\n",
        "fig.update_layout(\n",
        "    xaxis_title='Age',\n",
        "    yaxis_title='Salary',\n",
        "    title='Outlier Handling'\n",
        ")\n",
        "\n",
        "# Show the plot\n",
        "fig.show()"
      ]
    },
    {
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      "execution_count": null,
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        },
        "id": "1dJwsVekXDiG",
        "outputId": "a6dbcfdb-754d-4352-e237-c8aa33bb8adf"
      },
      "outputs": [
        {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Method 2: Trimming\n",
        "def trim(data, col, q):\n",
        "    lower = data[col].quantile(q)\n",
        "    upper = data[col].quantile(1 - q)\n",
        "    data = data[(data[col] >= lower) & (data[col] <= upper)]\n",
        "    return data\n",
        "\n",
        "# Apply trimming to Salary column\n",
        "mod_data = trim(data, 'Salary', 0.05)\n",
        "\n",
        "# Create a scatter plot\n",
        "fig = go.Figure(data=go.Scatter(x=mod_data['Age'], y=mod_data['Salary'], mode='markers'))\n",
        "\n",
        "# Update marker settings\n",
        "fig.update_traces(marker=dict(symbol='square', size=10))\n",
        "\n",
        "# Add axis labels and title\n",
        "fig.update_layout(\n",
        "    xaxis_title='Age',\n",
        "    yaxis_title='Salary',\n",
        "    title='Outlier Handling'\n",
        ")\n",
        "\n",
        "# Show the plot\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "4-_1oESoY1oH",
        "outputId": "9fed2499-8ab5-4f02-d7a7-e9ec94e59367"
      },
      "outputs": [
        {
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          "metadata": {},
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      ],
      "source": [
        "# Method 3: Z-score\n",
        "def remove_outliers_zscore(data, col, threshold):\n",
        "    z_scores = (data[col] - data[col].mean()) / data[col].std()\n",
        "    data = data[abs(z_scores) < threshold]\n",
        "    return data\n",
        "\n",
        "# Apply z-score method to Age column\n",
        "mod_data = remove_outliers_zscore(data, 'Age', 2)\n",
        "\n",
        "# Create a scatter plot\n",
        "fig = go.Figure(data=go.Scatter(x=mod_data['Age'], y=mod_data['Salary'], mode='markers'))\n",
        "\n",
        "# Update marker settings\n",
        "fig.update_traces(marker=dict(symbol='square', size=10))\n",
        "\n",
        "# Add axis labels and title\n",
        "fig.update_layout(\n",
        "    xaxis_title='Age',\n",
        "    yaxis_title='Salary',\n",
        "    title='Outlier Handling'\n",
        ")\n",
        "\n",
        "# Show the plot\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Week-2 : Day - 2**\n",
        "\n",
        "### Data Normalization and Scaling Methods\n",
        "\n",
        "**Min-Max Scaling:** This technique scales the data to a specific range, typically between 0 and 1. It preserves the relative relationships between the data points. It is suitable when the distribution of the data is not necessarily Gaussian.\n",
        "\n",
        "**Example:** In the code, we use MinMaxScaler from scikit-learn to scale the financial data. The transformed data will have values between 0 and 1.\n",
        "\n",
        "**Standardization:** This technique transforms the data to have a mean of 0 and a standard deviation of 1. It is suitable when the data follows a Gaussian distribution and is sensitive to outliers.\n",
        "\n",
        "**Example:** In the code, we use StandardScaler from scikit-learn to standardize the financial data. The transformed data will have a mean of 0 and a standard deviation of 1."
      ],
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      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PBZlocJxZJFK",
        "outputId": "e1a42172-c14b-498a-cbe9-af136934c9f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Financial Data:\n",
            "   Stock_Price  Volume  Market_Cap\n",
            "0          100   10000     1000000\n",
            "1          150   15000     2000000\n",
            "2          200    5000     1500000\n",
            "3           75    8000      500000\n",
            "4          125   12000     3000000\n",
            "\n",
            "Min-Max Scaled Financial Data:\n",
            "   Stock_Price  Volume  Market_Cap\n",
            "0          0.2     0.5         0.2\n",
            "1          0.6     1.0         0.6\n",
            "2          1.0     0.0         0.4\n",
            "3          0.0     0.3         0.0\n",
            "4          0.4     0.7         1.0\n",
            "\n",
            "Standardized Financial Data:\n",
            "   Stock_Price    Volume  Market_Cap\n",
            "0    -0.697486  0.000000   -0.697486\n",
            "1     0.464991  1.468051    0.464991\n",
            "2     1.627467 -1.468051   -0.116248\n",
            "3    -1.278724 -0.587220   -1.278724\n",
            "4    -0.116248  0.587220    1.627467\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "\n",
        "# Create dummy financial data\n",
        "financial_data = pd.DataFrame({\n",
        "    'Stock_Price': [100, 150, 200, 75, 125],\n",
        "    'Volume': [10000, 15000, 5000, 8000, 12000],\n",
        "    'Market_Cap': [1000000, 2000000, 1500000, 500000, 3000000]\n",
        "})\n",
        "\n",
        "# Print the original financial data\n",
        "print(\"Original Financial Data:\")\n",
        "print(financial_data)\n",
        "\n",
        "# Min-Max Scaling\n",
        "minmax_scaler = MinMaxScaler()\n",
        "minmax_scaled_data = minmax_scaler.fit_transform(financial_data)\n",
        "minmax_scaled_df = pd.DataFrame(minmax_scaled_data, columns=financial_data.columns)\n",
        "\n",
        "# Print the Min-Max scaled financial data\n",
        "print(\"\\nMin-Max Scaled Financial Data:\")\n",
        "print(minmax_scaled_df)\n",
        "\n",
        "# Standardization\n",
        "standard_scaler = StandardScaler()\n",
        "standard_scaled_data = standard_scaler.fit_transform(financial_data)\n",
        "standard_scaled_df = pd.DataFrame(standard_scaled_data, columns=financial_data.columns)\n",
        "\n",
        "# Print the standardized financial data\n",
        "print(\"\\nStandardized Financial Data:\")\n",
        "print(standard_scaled_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **When to use Which Technique?**\n",
        "\n",
        "**Min-Max Scaling (Normalization):**\n",
        "1. Use Min-Max scaling when you want to preserve the relative relationships between the data points.\n",
        "2. It is suitable when you have a specific range (e.g., 0 to 1) in which you want to scale the data.\n",
        "3. Min-Max scaling is helpful when the distribution of the data is not necessarily Gaussian and when you don't have strong outliers.\n",
        "4. It is commonly used in scenarios where the exact values and their relationships are important, such as when interpreting index values or comparing values within a specific range.\n",
        "\n",
        "\n",
        "**Standardization (Z-score Scaling):**\n",
        "\n",
        "1. Use standardization when you want to transform the data to have a mean of 0 and a standard deviation of 1.\n",
        "2. It is suitable when the data follows a Gaussian distribution or when your analysis or modeling techniques assume or perform better with standardized data.\n",
        "3. Standardization is robust against outliers, making it useful when dealing with datasets that have extreme values.\n",
        "4. It is commonly used in statistical analyses, machine learning algorithms (e.g., PCA), and when comparing variables with different units and scales."
      ],
      "metadata": {
        "id": "6v8BGC_YnsrR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Handling Categorical Data in Finance"
      ],
      "metadata": {
        "id": "-iciphA4vQGv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Create dummy financial data\n",
        "financial_data = pd.DataFrame({\n",
        "    'News_Sentiment': ['Positive', 'Negative', 'Neutral', 'Positive', 'Positive'],\n",
        "    'Stock_Price': [100, 150, 200, 75, 125],\n",
        "    'Volume': [10000, 15000, 5000, 8000, 12000]\n",
        "})\n",
        "\n",
        "# Print the original financial data\n",
        "print(\"Original Financial Data:\")\n",
        "print(financial_data)\n",
        "\n",
        "# Day 3: Handling Categorical Data and Encoding Techniques\n",
        "\n",
        "# Label Encoding for News Sentiment\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "financial_data['Encoded_Sentiment'] = label_encoder.fit_transform(financial_data['News_Sentiment'])\n",
        "\n",
        "# Print the encoded financial data\n",
        "print(\"\\nEncoded Financial Data (Label Encoding):\")\n",
        "print(financial_data)\n",
        "\n",
        "# One-Hot Encoding for News Sentiment\n",
        "onehot_encoder = OneHotEncoder(sparse=False)\n",
        "encoded_features = onehot_encoder.fit_transform(financial_data[['Encoded_Sentiment']])\n",
        "encoded_df = pd.DataFrame(encoded_features, columns=[f'Encoded_Sentiment_{value}' for value in label_encoder.classes_])\n",
        "financial_data_encoded = pd.concat([financial_data, encoded_df], axis=1)\n",
        "\n",
        "# Print the encoded financial data\n",
        "print(\"\\nEncoded Financial Data (One-Hot Encoding):\")\n",
        "print(financial_data_encoded)\n",
        "\n",
        "# Day 4: Feature Engineering and Selection\n",
        "\n",
        "# Feature Engineering - Create a new feature: Stock Price to Volume Ratio\n",
        "financial_data_encoded['Price_to_Volume_Ratio'] = financial_data_encoded['Stock_Price'] / financial_data_encoded['Volume']\n",
        "\n",
        "# Print the feature-engineered financial data\n",
        "print(\"\\nFeature-Engineered Financial Data:\")\n",
        "print(financial_data_encoded)\n",
        "\n",
        "# Discretize target variable into classes\n",
        "threshold = 0.01\n",
        "financial_data_encoded['Price_Class'] = financial_data_encoded['Price_to_Volume_Ratio'].apply(lambda x: 'Positive' if x >= threshold else 'Negative')\n",
        "\n",
        "# Feature Selection - Select top K features based on F-regression score\n",
        "X = financial_data_encoded[['Encoded_Sentiment_Negative', 'Encoded_Sentiment_Neutral', 'Encoded_Sentiment_Positive', 'Stock_Price', 'Volume']]\n",
        "y = financial_data_encoded['Price_Class']\n",
        "selector = SelectKBest(score_func=f_classif, k=2)\n",
        "selected_features = selector.fit_transform(X, y)\n",
        "selected_features_df = pd.DataFrame(selected_features, columns=X.columns[selector.get_support()])\n",
        "\n",
        "# Print the selected features\n",
        "print(\"\\nSelected Features:\")\n",
        "print(selected_features_df)\n",
        "print(y)\n",
        "\n",
        "# Day 5: Predicting Stock Price Movements Based on Financial News Sentiment Analysis\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(selected_features_df, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train a logistic regression model\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "# Print the accuracy\n",
        "print(\"\\nAccuracy of Predicting Stock Price Movements:\")\n",
        "print(accuracy)"
      ],
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          "text": [
            "Original Financial Data:\n",
            "  News_Sentiment  Stock_Price  Volume\n",
            "0       Positive          100   10000\n",
            "1       Negative          150   15000\n",
            "2        Neutral          200    5000\n",
            "3       Positive           75    8000\n",
            "4       Positive          125   12000\n",
            "\n",
            "Encoded Financial Data (Label Encoding):\n",
            "  News_Sentiment  Stock_Price  Volume  Encoded_Sentiment\n",
            "0       Positive          100   10000                  2\n",
            "1       Negative          150   15000                  0\n",
            "2        Neutral          200    5000                  1\n",
            "3       Positive           75    8000                  2\n",
            "4       Positive          125   12000                  2\n",
            "\n",
            "Encoded Financial Data (One-Hot Encoding):\n",
            "  News_Sentiment  Stock_Price  Volume  Encoded_Sentiment  \\\n",
            "0       Positive          100   10000                  2   \n",
            "1       Negative          150   15000                  0   \n",
            "2        Neutral          200    5000                  1   \n",
            "3       Positive           75    8000                  2   \n",
            "4       Positive          125   12000                  2   \n",
            "\n",
            "   Encoded_Sentiment_Negative  Encoded_Sentiment_Neutral  \\\n",
            "0                         0.0                        0.0   \n",
            "1                         1.0                        0.0   \n",
            "2                         0.0                        1.0   \n",
            "3                         0.0                        0.0   \n",
            "4                         0.0                        0.0   \n",
            "\n",
            "   Encoded_Sentiment_Positive  \n",
            "0                         1.0  \n",
            "1                         0.0  \n",
            "2                         0.0  \n",
            "3                         1.0  \n",
            "4                         1.0  \n",
            "\n",
            "Feature-Engineered Financial Data:\n",
            "  News_Sentiment  Stock_Price  Volume  Encoded_Sentiment  \\\n",
            "0       Positive          100   10000                  2   \n",
            "1       Negative          150   15000                  0   \n",
            "2        Neutral          200    5000                  1   \n",
            "3       Positive           75    8000                  2   \n",
            "4       Positive          125   12000                  2   \n",
            "\n",
            "   Encoded_Sentiment_Negative  Encoded_Sentiment_Neutral  \\\n",
            "0                         0.0                        0.0   \n",
            "1                         1.0                        0.0   \n",
            "2                         0.0                        1.0   \n",
            "3                         0.0                        0.0   \n",
            "4                         0.0                        0.0   \n",
            "\n",
            "   Encoded_Sentiment_Positive  Price_to_Volume_Ratio  \n",
            "0                         1.0               0.010000  \n",
            "1                         0.0               0.010000  \n",
            "2                         0.0               0.040000  \n",
            "3                         1.0               0.009375  \n",
            "4                         1.0               0.010417  \n",
            "\n",
            "Selected Features:\n",
            "   Encoded_Sentiment_Positive  Stock_Price\n",
            "0                         1.0        100.0\n",
            "1                         0.0        150.0\n",
            "2                         0.0        200.0\n",
            "3                         1.0         75.0\n",
            "4                         1.0        125.0\n",
            "0    Positive\n",
            "1    Positive\n",
            "2    Positive\n",
            "3    Negative\n",
            "4    Positive\n",
            "Name: Price_Class, dtype: object\n",
            "\n",
            "Accuracy of Predicting Stock Price Movements:\n",
            "1.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf\n",
        "from statsmodels.genmod.families import Poisson\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "\n",
        "# Create dummy data\n",
        "data = pd.DataFrame({\n",
        "    'Exposure': [11, 15, 20, 25, 31, 14, 19, 24, 29, 35, 12, 17, 23, 27, 33],\n",
        "    'Event_Count': [10, 15, 18, 22, 30, 13, 17, 21, 26, 32, 16, 20, 25, 28, 34],\n",
        "    'Predictor1': [1, 2, 3, 4, 9, 3, 4, 5, 6, 8, 2, 5, 7, 8, 10],\n",
        "    'Predictor2': [2, 4, 6, 7, 10, 4, 6, 8, 9, 11, 5, 8, 9, 11, 12]\n",
        "})\n",
        "\n",
        "x = data[['Exposure','Predictor1','Predictor2']]\n",
        "y = data[['Event_Count']]\n",
        "exog, endog = sm.add_constant(x), y\n",
        "mod = sm.GLM(endog, exog, family=sm.families.Poisson())\n",
        "res = mod.fit()\n",
        "print(res.summary())\n",
        "\n",
        "# Residuals analysis\n",
        "residuals = res.resid_pearson\n",
        "print(residuals)\n",
        "\n",
        "# Independence assumption: Plot residuals against predicted values\n",
        "sns.scatterplot(x=res.fittedvalues, y=residuals)\n",
        "plt.axhline(y=0, color='r', linestyle='--')\n",
        "plt.xlabel('Fitted Values')\n",
        "plt.ylabel('Residuals')\n",
        "plt.title('Residuals vs Fitted Values')\n",
        "plt.show() #Random So Holds\n",
        "\n",
        "# Equality of mean and variance assumption: Plot residuals against fitted values\n",
        "sns.scatterplot(x=res.fittedvalues, y=np.sqrt(np.abs(residuals)))\n",
        "plt.axhline(y=0, color='r', linestyle='--')\n",
        "plt.xlabel('Fitted Values')\n",
        "plt.ylabel('Square Root of Absolute Residuals')\n",
        "plt.title('Residuals vs Fitted Values')\n",
        "plt.show() #Met Homoscedasticity\n",
        "\n",
        "# Linearity assumption: Plot standardized residuals against predictors\n",
        "studentized_residuals = res.resid_pearson\n",
        "sns.scatterplot(x=data['Predictor1'], y=studentized_residuals)\n",
        "plt.axhline(y=0, color='r', linestyle='--')\n",
        "plt.xlabel('Predictor 1')\n",
        "plt.ylabel('Standardized Residuals')\n",
        "plt.title('Standardized Residuals vs Predictor 1')\n",
        "plt.show() # No Pattern So okey\n",
        "\n",
        "#suppress scientific notation\n",
        "import numpy as np\n",
        "np.set_printoptions(suppress=True)\n",
        "\n",
        "#create instance of influence\n",
        "influence = res.get_influence()\n",
        "\n",
        "#obtain Cook's distance for each observation\n",
        "cooks = influence.cooks_distance\n",
        "\n",
        "# Absence of influential outliers: Plot Cook's distance\n",
        "cooksd = influence.cooks_distance[0]\n",
        "plt.stem(np.arange(len(cooksd)), cooksd, markerfmt=',')\n",
        "plt.xlabel('Observation')\n",
        "plt.ylabel(\"Cook's Distance\")\n",
        "plt.title(\"Cook's Distance Plot\")\n",
        "plt.show() # We plot Cook's distance, which measures the influence of each observation on the regression coefficients. Observations with high Cook's distance values may be influential outliers."
      ],
      "metadata": {
        "id": "F0YIyZrxyHtX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62a8a0bc-b5c6-4b7f-ee97-e3a1fc2ac3fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Generalized Linear Model Regression Results                  \n",
            "==============================================================================\n",
            "Dep. Variable:            Event_Count   No. Observations:                   15\n",
            "Model:                            GLM   Df Residuals:                       11\n",
            "Model Family:                 Poisson   Df Model:                            3\n",
            "Link Function:                    Log   Scale:                          1.0000\n",
            "Method:                          IRLS   Log-Likelihood:                -36.950\n",
            "Date:                Mon, 17 Jul 2023   Deviance:                      0.79831\n",
            "Time:                        12:24:57   Pearson chi2:                    0.797\n",
            "No. Iterations:                     4   Pseudo R-squ. (CS):             0.8897\n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          2.1094      0.250      8.428      0.000       1.619       2.600\n",
            "Exposure       0.0140      0.018      0.784      0.433      -0.021       0.049\n",
            "Predictor1    -0.0045      0.076     -0.060      0.952      -0.153       0.144\n",
            "Predictor2     0.0843      0.081      1.041      0.298      -0.074       0.243\n",
            "==============================================================================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f7UVzsAFpb3L",
        "outputId": "247f5c99-ef64-4815-c4c1-d48ea04e0059"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    -0.397255\n",
            "1     0.230717\n",
            "2     0.031700\n",
            "3     0.272428\n",
            "4     0.293114\n",
            "5    -0.234288\n",
            "6    -0.128301\n",
            "7    -0.248493\n",
            "8     0.047351\n",
            "9    -0.152821\n",
            "10    0.327714\n",
            "11   -0.020856\n",
            "12    0.294457\n",
            "13   -0.254815\n",
            "14   -0.079254\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "L5JsidMarkh1",
        "outputId": "35a43b42-d7cb-4b8d-bbb5-e34c9271be06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "dZ3SRGt7rl3v",
        "outputId": "d12e56d9-1b3b-4b81-fb79-d13321f269c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "fPnBiOXDsC17",
        "outputId": "9d2d9fde-b17e-4eb6-e1d8-1d8b42a53ac6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#suppress scientific notation\n",
        "import numpy as np\n",
        "np.set_printoptions(suppress=True)\n",
        "\n",
        "#create instance of influence\n",
        "influence = res.get_influence()\n",
        "\n",
        "#obtain Cook's distance for each observation\n",
        "cooks = influence.cooks_distance\n",
        "\n",
        "# Absence of influential outliers: Plot Cook's distance\n",
        "cooksd = influence.cooks_distance[0]\n",
        "plt.stem(np.arange(len(cooksd)), cooksd, markerfmt=',')\n",
        "plt.xlabel('Observation')\n",
        "plt.ylabel(\"Cook's Distance\")\n",
        "plt.title(\"Cook's Distance Plot\")\n",
        "plt.show()\n",
        "\n",
        "# We plot Cook's distance, which measures the influence of each observation on the regression coefficients.\n",
        "# Observations with high Cook's distance values may be influential outliers."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "WJRGzbSJtAdQ",
        "outputId": "9b9f8ac5-d481-4032-e629-b49997b75263"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Feature Engineering in Financial Data**\n",
        "\n",
        "- Feature engineering is the process of creating new features from existing data or transforming existing features to improve the performance of machine learning models.\n",
        "\n",
        "- It involves selecting relevant features, handling missing data, encoding categorical variables, scaling numerical features, and creating new features that capture meaningful patterns in the data.\n",
        "\n",
        "**There are 4 ways to do it:**\n",
        "\n",
        "1. Handling missing data.\n",
        "2. Encoding Categorical Variables\n",
        "3. Feature Scaling\n",
        "4. Feature Selection"
      ],
      "metadata": {
        "id": "xlo25GM23Bdv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Load the dataset\n",
        "finance = {\n",
        "    'Date': pd.date_range(start='2023-01-01', periods=10, freq='D'),\n",
        "    'Stock': ['AAPL', 'GOOG', 'MSFT', 'AAPL', 'GOOG', 'MSFT', 'AAPL', 'GOOG', 'MSFT', 'AAPL'],\n",
        "    'Price': [150.25, 2700.50, 350.75, 152.10, 2750.20, 355.90, 148.50, 2725.30, 345.60, 155.75],\n",
        "    'Volume': [1000000, 800000, 1200000, 950000, 750000, 1100000, 900000, 850000, 1300000, 850000],\n",
        "    'Revenue': [1500000, 2160000, 4215000, 1440000, 2062500, 3914900, 1336500, 2385250, 4482000, 1355500],\n",
        "    'target' : [0,1,1,1,1,0,1,1,1,0]\n",
        "}\n",
        "\n",
        "# Create the DataFrame\n",
        "data = pd.DataFrame(finance)\n",
        "\n",
        "# Preprocess the data (handle missing values, encode categorical variables, etc.)\n",
        "\n",
        "\n",
        "# Categorical Variable Encoding\n",
        "label_encoder = LabelEncoder()\n",
        "data['Stock'] = label_encoder.fit_transform(data['Stock'])\n",
        "\n",
        "# Split data into features and target variable\n",
        "X = data.drop('target', axis=1)\n",
        "y = data['target']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "X_train.head()"
      ],
      "metadata": {
        "id": "DwzuYpJxyCR7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "5263c694-7d85-473c-9a0c-9be2878727b5"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date  Stock    Price   Volume  Revenue\n",
              "5 2023-01-06      2   355.90  1100000  3914900\n",
              "0 2023-01-01      0   150.25  1000000  1500000\n",
              "7 2023-01-08      1  2725.30   850000  2385250\n",
              "2 2023-01-03      2   350.75  1200000  4215000\n",
              "9 2023-01-10      0   155.75   850000  1355500"
            ],
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              "\n",
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              "      <td>2023-01-06</td>\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-ad510803-865e-4b2f-8843-6a3e9e778105 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3f15c6a0-a100-4ef8-8ed6-27b9e95655b0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3f15c6a0-a100-4ef8-8ed6-27b9e95655b0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature scaling\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train.drop('Date',axis=1))\n",
        "X_test_scaled = scaler.transform(X_test.drop('Date',axis=1))\n",
        "\n",
        "X_train_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kBUPQJvQ5dZG",
        "outputId": "e030b939-c45b-4f17-d87b-3df89e25a03e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.50755672, -0.45043069,  1.09544512,  1.50106239],\n",
              "       [-0.90453403, -0.63844012,  0.36514837, -0.71101389],\n",
              "       [ 0.30151134,  1.71572319, -0.73029674,  0.09988533],\n",
              "       [ 1.50755672, -0.45513893,  1.82574186,  1.77595746],\n",
              "       [-0.90453403, -0.63341191, -0.73029674, -0.84337755],\n",
              "       [ 0.30151134,  1.73848728, -1.46059349, -0.19575739],\n",
              "       [-0.90453403, -0.63674881,  0.        , -0.76597458],\n",
              "       [-0.90453403, -0.64004001, -0.36514837, -0.86078177]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature selection\n",
        "selector = SelectKBest(score_func=f_classif, k=4)\n",
        "X_train_selected = selector.fit_transform(X_train_scaled, y_train)\n",
        "X_test_selected = selector.transform(X_test_scaled)"
      ],
      "metadata": {
        "id": "jfJEctDj-fSA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a logistic regression model\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train_selected, y_train)\n",
        "\n",
        "# Make predictions and evaluate the model\n",
        "y_pred = model.predict(X_test_selected)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYDXIugi-CJR",
        "outputId": "8dcbfb10-ef33-4682-f2c1-5c6a8a2c942f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Project: Predicting stock price movements based on financial news sentiment analysis**\n",
        "\n",
        "This project aims to predict stock price movements (e.g., increase or decrease) based on financial news sentiment analysis. The steps involved in this project are as follows:\n",
        "\n",
        "- Data Collection: Collect financial news data and historical stock price data. The news data can be obtained from financial news websites or APIs, and stock price data can be obtained from financial data platforms.\n",
        "\n",
        "- Text Preprocessing: Preprocess the text data by removing stopwords, converting text to lowercase, and performing stemming or lemmatization.\n",
        "\n",
        "- Sentiment Analysis: Use natural language processing (NLP) techniques and sentiment analysis models to determine the sentiment (positive, negative, neutral) of each news article.\n",
        "\n",
        "- Feature Extraction: Convert the sentiment analysis results into numerical features that can be used for prediction.\n",
        "\n",
        "- Train and Test Model: Split the data into training and testing sets. Train a machine learning model (e.g., logistic regression, random forest, or neural network) using the training data.\n",
        "\n",
        "- Evaluate the Model: Test the model on the testing data and evaluate its performance using metrics like accuracy, precision, recall, and F1 score."
      ],
      "metadata": {
        "id": "tQ5uZQVT-pOC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from textblob import TextBlob\n",
        "import pandas as pd\n",
        "\n",
        "# Sample financial news data\n",
        "news_data = pd.DataFrame({\n",
        "    'headline': [\"Stocks surge on positive earnings report\",\n",
        "                \"Market uncertainty impacts stock prices\",\n",
        "                \"Investors optimistic about future growth\",\n",
        "                \"Company announces record-breaking sales\",\n",
        "                \"Economic downturn affects stock market\"]\n",
        "})\n",
        "\n",
        "# Perform sentiment analysis on the headlines\n",
        "news_data['sentiment'] = news_data['headline'].apply(lambda x: TextBlob(x).sentiment.polarity)\n",
        "\n",
        "# Assign labels based on sentiment\n",
        "news_data['label'] = news_data['sentiment'].apply(lambda x: 'Positive' if x > 0 else 'Negative' if x < 0 else 'Neutral')\n",
        "\n",
        "print(news_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pIa13KP-U2c",
        "outputId": "196f2106-a10f-4bc6-d2fe-0c52ca646e4f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                   headline  sentiment     label\n",
            "0  Stocks surge on positive earnings report   0.227273  Positive\n",
            "1   Market uncertainty impacts stock prices   0.000000   Neutral\n",
            "2  Investors optimistic about future growth   0.000000   Neutral\n",
            "3   Company announces record-breaking sales   0.000000   Neutral\n",
            "4    Economic downturn affects stock market   0.200000  Positive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Xrb7pADo-1NF"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}