Air quality prediction Assign 2.ipynb

{
  "cells": [
    {
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np \nimport pandas as pd \nfrom matplotlib import pyplot as plt\nimport seaborn as sns\nfrom sklearn.preprocessing import Normalizer\nfrom sklearn.model_selection import train_test_split",
      "execution_count": 37,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "trusted": true
      },
      "cell_type": "code",
      "source": "train = pd.read_csv('../input/Train.csv')\ntest = pd.read_csv('../input/Test.csv')",
      "execution_count": 38,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "88a5124859f23f4d812d2d5dc9026286731291ab"
      },
      "cell_type": "code",
      "source": "train.describe()",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 39,
          "data": {
            "text/plain": "         feature_1    feature_2     ...         feature_5       target\ncount  1600.000000  1600.000000     ...       1600.000000  1600.000000\nmean      0.004114    -0.027604     ...          0.012005     0.318835\nstd       0.997338     1.021776     ...          0.978654   110.741562\nmin      -3.380125    -3.314844     ...         -2.851707  -379.829794\n25%      -0.647165    -0.705030     ...         -0.615880   -71.897040\n50%      -0.000360    -0.043769     ...         -0.013527    -0.610665\n75%       0.682118     0.645442     ...          0.650089    71.226603\nmax       3.287205     3.438895     ...          3.321770   337.643014\n\n[8 rows x 6 columns]",
            "text/html": "<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>feature_1</th>\n      <th>feature_2</th>\n      <th>feature_3</th>\n      <th>feature_4</th>\n      <th>feature_5</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1600.000000</td>\n      <td>1600.000000</td>\n      <td>1600.000000</td>\n      <td>1600.000000</td>\n      <td>1600.000000</td>\n      <td>1600.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.004114</td>\n      <td>-0.027604</td>\n      <td>-0.044459</td>\n      <td>-0.042408</td>\n      <td>0.012005</td>\n      <td>0.318835</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.997338</td>\n      <td>1.021776</td>\n      <td>1.011768</td>\n      <td>1.017189</td>\n      <td>0.978654</td>\n      <td>110.741562</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-3.380125</td>\n      <td>-3.314844</td>\n      <td>-3.153118</td>\n      <td>-3.250169</td>\n      <td>-2.851707</td>\n      <td>-379.829794</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-0.647165</td>\n      <td>-0.705030</td>\n      <td>-0.706385</td>\n      <td>-0.709504</td>\n      <td>-0.615880</td>\n      <td>-71.897040</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-0.000360</td>\n      <td>-0.043769</td>\n      <td>-0.041272</td>\n      <td>-0.029756</td>\n      <td>-0.013527</td>\n      <td>-0.610665</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.682118</td>\n      <td>0.645442</td>\n      <td>0.639130</td>\n      <td>0.646290</td>\n      <td>0.650089</td>\n      <td>71.226603</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.287205</td>\n      <td>3.438895</td>\n      <td>3.216176</td>\n      <td>2.985410</td>\n      <td>3.321770</td>\n      <td>337.643014</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "05572db32c2c5b43fc51e4be47a6eed206c7a9bf"
      },
      "cell_type": "code",
      "source": "test.describe()",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "data": {
            "text/plain": "        feature_1   feature_2     ...       feature_5      target\ncount  400.000000  400.000000     ...      400.000000  400.000000\nmean    -0.021804   -0.012806     ...        0.031016    2.988897\nstd      0.979163    1.021580     ...        0.937469  114.139329\nmin     -2.564288   -2.992849     ...       -2.493446 -320.048473\n25%     -0.784581   -0.640843     ...       -0.589199  -65.763521\n50%     -0.004759   -0.077186     ...        0.047327    0.699553\n75%      0.681777    0.738286     ...        0.586405   83.257690\nmax      3.244870    2.591723     ...        2.870382  335.497450\n\n[8 rows x 6 columns]",
            "text/html": "<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>feature_1</th>\n      <th>feature_2</th>\n      <th>feature_3</th>\n      <th>feature_4</th>\n      <th>feature_5</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>400.000000</td>\n      <td>400.000000</td>\n      <td>400.000000</td>\n      <td>400.000000</td>\n      <td>400.000000</td>\n      <td>400.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>-0.021804</td>\n      <td>-0.012806</td>\n      <td>-0.010988</td>\n      <td>-0.034864</td>\n      <td>0.031016</td>\n      <td>2.988897</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.979163</td>\n      <td>1.021580</td>\n      <td>1.041857</td>\n      <td>1.034507</td>\n      <td>0.937469</td>\n      <td>114.139329</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-2.564288</td>\n      <td>-2.992849</td>\n      <td>-2.767818</td>\n      <td>-3.041550</td>\n      <td>-2.493446</td>\n      <td>-320.048473</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-0.784581</td>\n      <td>-0.640843</td>\n      <td>-0.780267</td>\n      <td>-0.730596</td>\n      <td>-0.589199</td>\n      <td>-65.763521</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-0.004759</td>\n      <td>-0.077186</td>\n      <td>-0.109300</td>\n      <td>-0.040752</td>\n      <td>0.047327</td>\n      <td>0.699553</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.681777</td>\n      <td>0.738286</td>\n      <td>0.754063</td>\n      <td>0.656363</td>\n      <td>0.586405</td>\n      <td>83.257690</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.244870</td>\n      <td>2.591723</td>\n      <td>2.909357</td>\n      <td>2.662180</td>\n      <td>2.870382</td>\n      <td>335.497450</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "32f8a68735d5e2697cc78524dc914b37081d8fb4"
      },
      "cell_type": "code",
      "source": "train.isnull().sum()",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": "feature_1    0\nfeature_2    0\nfeature_3    0\nfeature_4    0\nfeature_5    0\ntarget       0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "1910c29a42d148ec0e40239eab2931c704bfd215"
      },
      "cell_type": "code",
      "source": "test.isnull().sum()",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 42,
          "data": {
            "text/plain": "feature_1    0\nfeature_2    0\nfeature_3    0\nfeature_4    0\nfeature_5    0\ntarget       0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "dc9d0f2fa9b161daca5689fe4661e8a1766ca7be"
      },
      "cell_type": "markdown",
      "source": "### So we got cleaned data\n\nAll are contineous"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "1d253a677083c15845c8be12a09186cfbbb90273"
      },
      "cell_type": "code",
      "source": "#plotting correlations\nnum_feat=train.columns[train.dtypes!=object]\nnum_feat=num_feat[1:-1] \nlabels = []\nvalues = []\nfor col in num_feat:\n    labels.append(col)\n    values.append(np.corrcoef(train[col].values, train.target.values)[0,1])\n\nind = np.arange(len(labels))\nwidth = 0.9\nfig, ax = plt.subplots(figsize=(12,8))\nrects = ax.barh(ind, np.array(values), color='red')\nax.set_yticks(ind+((width)/2.))\nax.set_yticklabels(labels, rotation='horizontal')\nax.set_xlabel(\"Correlation coefficient\")\nax.set_title(\"Correlation Coefficients w.r.t target\");",
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 864x576 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "de3f7002433686d14f0f5c28de16f3b62e870b52"
      },
      "cell_type": "code",
      "source": "#Heatmap\ncorrMatrix=train[num_feat].corr()\nsns.set(font_scale=1.10)\nplt.figure(figsize=(10, 10))\nsns.heatmap(corrMatrix, vmax=.8, linewidths=0.01,\n            square=True,annot=True,cmap='viridis',linecolor=\"white\")\nplt.title('Correlation between features');",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x720 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "ac967b9159bea42193919b91b15affae5bd54e02"
      },
      "cell_type": "code",
      "source": "plt.hist((train.target),bins=152)\nplt.show()",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "3e119d4fff068f6c75f02afbf4c45593b21f9b33"
      },
      "cell_type": "code",
      "source": "# Let's see how the numeric data is distributed.\n\ntrain.hist(bins=10, figsize=(20,15), color='#E14906')\nplt.show()",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1440x1080 with 6 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "7f454e029b6d807455f3d953ef73c64bb044a8ac"
      },
      "cell_type": "markdown",
      "source": "# Linear Regression \n\nBy merging train.csv and test.csv data and then splitting in 80:20 ratio\n\nthen creating model\n\nand calculating score"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "2432ebac80232f6c26923245855a4c9d8424a3fa"
      },
      "cell_type": "code",
      "source": "combined = train.append(test)\ncombined.reset_index(inplace=True)\n",
      "execution_count": 47,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "e2e67b248f2e970ffaaca7e2c286a165d27f77a0"
      },
      "cell_type": "code",
      "source": "combined.describe()",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 48,
          "data": {
            "text/plain": "             index    feature_1     ...         feature_5       target\ncount  2000.000000  2000.000000     ...       2000.000000  2000.000000\nmean    679.500000    -0.001069     ...          0.015808     0.852848\nstd     480.675332     0.993540     ...          0.970356   111.405622\nmin       0.000000    -3.380125     ...         -2.851707  -379.829794\n25%     249.750000    -0.689607     ...         -0.612389   -70.173508\n50%     599.500000    -0.000780     ...         -0.004115    -0.466188\n75%    1099.250000     0.682118     ...          0.635205    73.638671\nmax    1599.000000     3.287205     ...          3.321770   337.643014\n\n[8 rows x 7 columns]",
            "text/html": "<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>index</th>\n      <th>feature_1</th>\n      <th>feature_2</th>\n      <th>feature_3</th>\n      <th>feature_4</th>\n      <th>feature_5</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2000.000000</td>\n      <td>2000.000000</td>\n      <td>2000.000000</td>\n      <td>2000.000000</td>\n      <td>2000.000000</td>\n      <td>2000.000000</td>\n      <td>2000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>679.500000</td>\n      <td>-0.001069</td>\n      <td>-0.024644</td>\n      <td>-0.037765</td>\n      <td>-0.040899</td>\n      <td>0.015808</td>\n      <td>0.852848</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>480.675332</td>\n      <td>0.993540</td>\n      <td>1.021498</td>\n      <td>1.017681</td>\n      <td>1.020420</td>\n      <td>0.970356</td>\n      <td>111.405622</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>-3.380125</td>\n      <td>-3.314844</td>\n      <td>-3.153118</td>\n      <td>-3.250169</td>\n      <td>-2.851707</td>\n      <td>-379.829794</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>249.750000</td>\n      <td>-0.689607</td>\n      <td>-0.683679</td>\n      <td>-0.730135</td>\n      <td>-0.716403</td>\n      <td>-0.612389</td>\n      <td>-70.173508</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>599.500000</td>\n      <td>-0.000780</td>\n      <td>-0.047127</td>\n      <td>-0.049629</td>\n      <td>-0.033136</td>\n      <td>-0.004115</td>\n      <td>-0.466188</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1099.250000</td>\n      <td>0.682118</td>\n      <td>0.661455</td>\n      <td>0.647560</td>\n      <td>0.648268</td>\n      <td>0.635205</td>\n      <td>73.638671</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1599.000000</td>\n      <td>3.287205</td>\n      <td>3.438895</td>\n      <td>3.216176</td>\n      <td>2.985410</td>\n      <td>3.321770</td>\n      <td>337.643014</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f2dfa829d493e9f39d431a001add261efdc67932"
      },
      "cell_type": "code",
      "source": "y = combined['target']\nx = combined.drop('target', axis=1)",
      "execution_count": 49,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "3e3cbaf617d5e2fd86cbbef49f1bc2745eb66794"
      },
      "cell_type": "code",
      "source": "y.describe()",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 50,
          "data": {
            "text/plain": "count    2000.000000\nmean        0.852848\nstd       111.405622\nmin      -379.829794\n25%       -70.173508\n50%        -0.466188\n75%        73.638671\nmax       337.643014\nName: target, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "5ef4617d61a62487f8573a31ca613cabc47a2200"
      },
      "cell_type": "code",
      "source": "x.describe()",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 51,
          "data": {
            "text/plain": "             index    feature_1     ...         feature_4    feature_5\ncount  2000.000000  2000.000000     ...       2000.000000  2000.000000\nmean    679.500000    -0.001069     ...         -0.040899     0.015808\nstd     480.675332     0.993540     ...          1.020420     0.970356\nmin       0.000000    -3.380125     ...         -3.250169    -2.851707\n25%     249.750000    -0.689607     ...         -0.716403    -0.612389\n50%     599.500000    -0.000780     ...         -0.033136    -0.004115\n75%    1099.250000     0.682118     ...          0.648268     0.635205\nmax    1599.000000     3.287205     ...          2.985410     3.321770\n\n[8 rows x 6 columns]",
            "text/html": "<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>index</th>\n      <th>feature_1</th>\n      <th>feature_2</th>\n      <th>feature_3</th>\n      <th>feature_4</th>\n      <th>feature_5</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2000.000000</td>\n      <td>2000.000000</td>\n      <td>2000.000000</td>\n      <td>2000.000000</td>\n      <td>2000.000000</td>\n      <td>2000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>679.500000</td>\n      <td>-0.001069</td>\n      <td>-0.024644</td>\n      <td>-0.037765</td>\n      <td>-0.040899</td>\n      <td>0.015808</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>480.675332</td>\n      <td>0.993540</td>\n      <td>1.021498</td>\n      <td>1.017681</td>\n      <td>1.020420</td>\n      <td>0.970356</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>-3.380125</td>\n      <td>-3.314844</td>\n      <td>-3.153118</td>\n      <td>-3.250169</td>\n      <td>-2.851707</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>249.750000</td>\n      <td>-0.689607</td>\n      <td>-0.683679</td>\n      <td>-0.730135</td>\n      <td>-0.716403</td>\n      <td>-0.612389</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>599.500000</td>\n      <td>-0.000780</td>\n      <td>-0.047127</td>\n      <td>-0.049629</td>\n      <td>-0.033136</td>\n      <td>-0.004115</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1099.250000</td>\n      <td>0.682118</td>\n      <td>0.661455</td>\n      <td>0.647560</td>\n      <td>0.648268</td>\n      <td>0.635205</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1599.000000</td>\n      <td>3.287205</td>\n      <td>3.438895</td>\n      <td>3.216176</td>\n      <td>2.985410</td>\n      <td>3.321770</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "799585fde66d80252db6532359b88afda19b40c7"
      },
      "cell_type": "markdown",
      "source": "### Splitting into 80:20"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "53d89154592627ff866af75915035af84de37a70"
      },
      "cell_type": "code",
      "source": "X_train, X_test, y_train, y_test = train_test_split(x, y ,test_size=0.2)",
      "execution_count": 52,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "97609d455abbf27f7abb7f5e4161a28caa34818b"
      },
      "cell_type": "code",
      "source": "print(X_train.shape)\nprint(X_test.shape)\nprint(y_train.shape)\nprint(y_test.shape)",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": "(1600, 6)\n(400, 6)\n(1600,)\n(400,)\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "6f9ebe8bd8d464e2ae3dafe672c181617e5117d0"
      },
      "cell_type": "code",
      "source": "# Implimenting Linear Regression from inbuilt function of sklearn\nfrom sklearn.linear_model import LinearRegression\nreg=LinearRegression(fit_intercept=True)\nmodel = reg.fit(X_train,y_train)\npredict = model.predict(X_test)",
      "execution_count": 54,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "c6f3233bb7d509febaa85c4142c7709d6c8fc321"
      },
      "cell_type": "code",
      "source": "#Predicting Score\nfrom sklearn.metrics import r2_score\nprint(r2_score(y_test,predict))",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": "0.9652683982412767\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f61f6f029e564a3ee6f810770373c2727987141a"
      },
      "cell_type": "code",
      "source": "print(\"Training Score %.4f\"%reg.score(X_train,y_train))\nprint(\"Testing Score %.4f\"%reg.score(X_test,y_test))",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Training Score 0.9671\nTesting Score 0.9653\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "b6072ac980d5701f40ed4d365d1b3f5648fc836f"
      },
      "cell_type": "markdown",
      "source": "# Cross Validation"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a56ccd061673c1885eef5a0f09ff7212ab8c25fe"
      },
      "cell_type": "code",
      "source": "from sklearn.model_selection import cross_val_score",
      "execution_count": 75,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "8ab504d53469e7d4291a767f8fb1894fab8fa924"
      },
      "cell_type": "code",
      "source": "scores=cross_val_score(reg,X_train,y_train,cv=10,scoring='r2')",
      "execution_count": 77,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f6d28621f5672bcc6620d92588e08a68528c35f4"
      },
      "cell_type": "code",
      "source": "print(scores)",
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[0.9742942  0.9690659  0.96528537 0.97470028 0.9549502  0.97095972\n 0.96522781 0.95813844 0.96009921 0.96945765]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b85a7db5e05b4a3692af06d94df94c64f61ee74e"
      },
      "cell_type": "code",
      "source": "print(scores.mean())",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": "0.9662178757288962\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b6f9755a03cd628e97aa275ccb168f8655b309a5"
      },
      "cell_type": "code",
      "source": "print(scores.std())",
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": "0.006401534297996244\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "eaab54fc174ec255ecf966050e16b5caa0a7b73e"
      },
      "cell_type": "markdown",
      "source": "### Calculating loss\n"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "7b35483c6f4d38e0deea23b96b3475fd10ed321b"
      },
      "cell_type": "code",
      "source": "scores=cross_val_score(reg,X_train,y_train,cv=10,scoring='neg_mean_squared_error')",
      "execution_count": 90,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "67591de58fb0e239a9037cff9ad60cfd9ade97bd"
      },
      "cell_type": "code",
      "source": "print(scores)",
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[-350.69193093 -364.41648534 -454.75151141 -339.1719146  -537.54033008\n -408.73147461 -404.28760396 -407.55332667 -464.67421468 -380.12198977]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "535e5f2fbb9a7e18d4501e4a9fa502618472e14d"
      },
      "cell_type": "code",
      "source": "#Average loss\nprint(scores.mean())",
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": "-411.19407820544376\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "c093ddd7938e56abee4afa75db98da8c847918d3"
      },
      "cell_type": "code",
      "source": "print(scores.std())",
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": "57.26175771935867\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "22c1f553b37ada1aa2115426d74ca229d960af34"
      },
      "cell_type": "markdown",
      "source": "## Linear Regression fit"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "5f1260135f3d9aaccf610ba84fe41ea0ad0e8adb"
      },
      "cell_type": "code",
      "source": "y_pred = reg.predict(X_test)\nplt.figure(figsize=(10, 5))\nplt.scatter(y_test, y_pred, s=20)\nplt.title('Predicted vs. Actual')\nplt.xlabel('Actual target')\nplt.ylabel('Predicted target')\n\nplt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)],'r')\nplt.tight_layout()",
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x360 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "3104dbff01f0ae037e1966f0a033fb75ed686976"
      },
      "cell_type": "markdown",
      "source": "### Predicted Values"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "616fdadd1b764ff29397ee7cac9a873998b0064a"
      },
      "cell_type": "code",
      "source": "y_pred.shape",
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 69,
          "data": {
            "text/plain": "(400,)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "1028f1fc6e4afcd36b4b6ba1351233657a0ae359"
      },
      "cell_type": "code",
      "source": "print(\"predicted values for 400 test data are\", y_pred)",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": "predicted values for 400 test data are [ 2.68552785e+02  9.45836446e+01  5.86871511e+01  4.76337354e+01\n -2.70691582e+02  9.20312800e+01  6.56367175e+01 -6.70386066e+01\n  7.20393873e+01 -4.71882254e+01  1.13772616e+02 -1.05961668e+02\n -5.87422314e+01  1.26520287e+02  4.66802155e+01 -1.17279890e+02\n -3.55121809e+00 -2.61270314e+01 -4.83740239e+01  8.03006385e+01\n -3.58760910e+01  3.03495095e+01 -3.87767459e+01 -3.28092065e+01\n  8.68540114e+00 -2.36219167e+02 -3.13249786e+01 -1.13216958e+02\n  3.96832799e+01 -4.02290193e+01 -1.80653637e+02 -8.70352640e+01\n  9.50859648e+01  3.98694188e+01  2.23196185e+01  1.15097928e+02\n -1.12294254e+01 -2.13901415e+02  2.02754750e+01  2.07702100e+02\n -2.77552538e+01 -1.30301541e+02  9.12832269e+01 -1.93027949e+01\n -7.81842920e+01  6.45077402e+00  1.72129857e+02 -8.80952865e+01\n -7.30861637e+01 -2.83531873e-01 -7.10613676e+01  1.35788277e+01\n  7.42549105e+01  1.25986571e+01 -1.34156524e+01 -4.29461128e+01\n -1.04619817e+02 -7.55890478e-01 -8.34267217e+01 -6.77773782e+01\n  4.12364625e+01  5.41566656e+01  2.50953334e+01 -5.07115619e+01\n  6.27444400e+01  5.80220900e+01 -1.78068159e+01  3.37818248e+00\n -1.15343581e+02 -1.84953111e+02  7.52028387e+01 -2.21397079e+02\n -3.02907666e-01 -1.63915102e+01  1.71518329e+01 -7.97754538e+01\n  8.40581207e+01 -1.39103103e+02  3.69737696e+00  1.56834547e+02\n  1.12933130e+02 -4.47487961e+01 -1.67604114e+02 -1.56315022e+02\n -2.88156392e+01  1.28789941e+02 -7.94183543e+01 -2.99673678e+01\n  1.22216498e+02  5.77621718e+01  4.05406909e+01  3.05313066e+01\n -2.46189088e+02 -2.51927326e+02 -2.30284889e+01  1.35588491e+02\n  8.38794579e+01  5.83999759e+01  2.57486921e+01 -8.53941270e+01\n -1.50327029e+02 -6.05987268e+01  4.68904222e+01  3.05437239e+01\n  1.18097878e+02  1.30918954e+02 -1.68884948e+01 -2.69528566e+01\n  1.58080928e+02 -1.73060527e+02  2.13976608e+02  1.12400774e+02\n  1.82434464e+02 -1.59788650e+02 -7.10434879e+01  2.12380076e+02\n -2.68726758e+02 -1.13466924e+01  1.39274248e+02 -3.93611549e+01\n  8.83569149e+01 -2.85543099e+01  6.25201256e+00  1.03628638e+02\n -1.11572887e+02 -7.96482710e+01 -3.31112180e+02  3.69092753e+01\n  4.80809370e+01 -5.59924047e+01  1.20993257e+01  2.49931498e+02\n -1.20966391e+02 -4.06533949e+00  1.01497994e+02 -2.27480208e+02\n -7.02464486e+01 -1.10614917e+02  1.43674252e+02  1.47301621e+02\n -1.60074726e+02  9.17939466e+00  4.77485515e+01  5.13799200e+01\n  1.74499890e+02  6.90136948e+01  1.97837028e+01 -1.98089114e+02\n -1.12317215e+02  7.43942547e+01 -1.29000297e+02 -5.97430894e+01\n -2.00290931e+02 -9.49987404e+01  9.30500025e+01 -1.04311680e+01\n -6.28684045e+01  6.32055323e+01  2.36231534e+02  4.70671656e+01\n -9.21132004e+01 -6.33336191e+01 -1.09048207e+02  1.72636015e+02\n -1.57391130e+02  1.43274738e+01 -1.19495243e+02 -1.85962144e+02\n  3.57756615e+01 -2.03218751e+01 -1.36631067e+02 -3.19897517e+01\n -9.30770515e+00 -1.46764366e+02 -3.16093749e+01  3.27568297e+01\n -1.20574027e+01  8.30650096e+01 -4.07511911e+01  6.44320289e+00\n -4.31521681e+01  3.23314483e+01 -4.36886523e+01  5.77756481e+01\n -2.52479785e+01  2.29634663e+01  4.67642514e+01 -1.04678488e+02\n -1.56775776e+02 -5.28544044e+00  2.26625130e+01 -3.63192190e+00\n -1.19767094e+02 -5.01381342e+01  6.34463047e+01  8.22689364e+01\n  5.13848028e+00  1.20663963e+02  3.30188351e+01 -1.80900108e+02\n  1.66920906e+02  4.17910902e+01  2.58836933e+01 -1.94446656e+01\n -1.25890114e+02 -1.09067831e+02  5.46607435e+01  9.42551413e+01\n -4.23912597e+01  1.85950728e+02 -3.88311967e+00  4.15954596e+01\n  1.89974384e+02 -7.07714583e+01  2.04593800e+02  3.98069979e+01\n  5.97937467e+01  1.23490910e+01  5.91294484e+01  6.41323966e+00\n -5.37421398e+01  5.41481902e+00 -2.57899156e+02 -2.05024177e+02\n -8.65963103e+01 -1.53745269e+02  6.41724221e+01  2.40781055e+02\n  5.28616331e+01  3.46743615e+01 -1.31694482e+02  2.08370646e+02\n  1.45846141e+01  2.12130831e+02  7.95877267e+01 -1.18068625e+02\n -1.84474627e+02 -6.01135033e+01  6.49257815e+01  1.43538566e+02\n -5.63680371e+01 -1.58547612e+02 -2.59192244e+01  2.98259570e+01\n  9.30650692e+01 -1.90545303e+01  1.13929753e+02  6.27341404e+01\n -1.30062270e+02  7.46063736e+01 -1.31976835e+02  2.50100241e+01\n -4.61524090e+01  3.43958434e+01 -2.32336446e+01  1.42746785e+02\n  1.20540518e+02  2.69739076e+01  1.25570397e+01 -9.54623127e+01\n -1.23885670e+01 -1.72891606e+02 -9.65938613e+01 -9.81258964e+00\n  8.47921728e+01  8.68836540e+01  1.71677613e+01  1.02080774e+02\n -4.21274782e+01 -6.97740017e+01  4.17007792e+01 -4.54019897e+01\n  5.97931007e+01  3.64885376e+01 -6.64872740e+01 -2.31163792e+01\n -4.93785691e+01 -1.42736370e+02  1.89357248e+02  1.52217759e+02\n -3.33601913e+02  4.66739016e+00 -1.14344419e+02 -2.20936848e+01\n  1.32183270e+02  1.31583616e+00 -6.77844724e+01 -1.58359923e+02\n  1.90800996e+00 -7.28292932e+01 -2.32836898e+01 -2.06550446e+02\n -1.21375254e+02 -7.82931450e+01  5.34716659e+01  2.93223768e+01\n  1.51456142e+02  3.65957924e+01 -8.24057286e+01 -1.28144452e+02\n -1.01770924e+02 -6.53826759e+01  1.72922622e+02  4.07869250e+01\n  1.65885333e+01 -1.16578101e+01  7.18883966e+01 -1.82481437e+02\n -2.33469380e+02 -1.71735634e+02 -4.56725424e+01  3.52308305e+01\n  3.94986235e+01 -9.31453224e+01  6.17532527e+01 -9.82941967e+01\n -1.78041883e+02 -1.59432131e+02  8.28020078e+01 -4.28021400e+01\n -1.25647821e+02 -1.13932395e+02 -1.01535210e+02  1.21130076e+02\n -1.32522843e+02  7.64303956e+01 -1.77015217e+02 -3.93621454e+01\n  1.49631219e+02 -2.91940039e+02 -2.16490666e+01 -3.62758806e+01\n  7.33401948e+01  3.62402335e+01 -3.89068810e+01  1.71026081e+02\n -1.72637727e+02 -1.71785762e+02  2.83124862e+01  1.00642859e+02\n  1.56767455e+00 -7.14003006e+01  1.34817967e+02 -1.55532000e+02\n -2.89587549e+00 -2.37057630e+01  6.56571679e+01 -4.68032428e+01\n  3.92067520e+01  2.06293156e+02  6.05303561e+01 -4.26979920e+01\n -1.36561354e+02  1.57135910e+02  2.45160552e+01  1.51128226e+01\n  1.14593244e+02 -5.48433827e+01 -1.55823155e+02  7.51885938e+01\n  2.03933419e+02 -4.66236270e+01  1.00564430e+02  1.50667704e+02\n -1.07051503e+02 -3.34472376e+01  4.26191524e+01  1.70890281e+02\n  1.30907570e+02 -4.23844706e+01  2.14810570e+02  2.13162456e+02\n -1.19165873e+02 -1.19736472e+02  5.84329535e+01 -3.94776845e+01\n -7.11341767e+01 -2.45933349e+02  1.59581930e+02 -4.89686455e+01\n  1.37691516e+02 -1.65660375e+02  1.55286147e+02 -4.48329821e+01\n -8.88275456e+00  3.36358833e+01  1.00009396e+02  4.74498272e+01\n -7.02278714e+01 -9.11579111e+00  1.10689097e+02 -7.64814354e+01\n  1.89876991e+01 -5.44579614e+01  3.34858065e+01 -4.38922006e+01\n -1.19954295e+02  2.64188695e+01 -9.28232264e+01  1.49855908e+02]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "d9a6e07d768f7fedb2082db189844f3053df609c"
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.6",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
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  "nbformat_minor": 1
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