Day 18.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wine dataset from (UCI) using the PCA technique for Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 1: Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the dataset\n",
    "dataset = pd.read_csv('Wine.csv')\n",
    "X = dataset.iloc[: , 0:13].values     # feature metric\n",
    "y = dataset.iloc[: , 13].values       # dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the dataset into test set and train set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y , test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 2: Applying the principal component analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the pca model\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# init the PCA model\n",
    "pca = PCA(n_components = 2)       # n_compent is 2 on the basis of high variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fitting the feature metric data into PCA model\n",
    "X_train = pca.fit_transform(X_train)\n",
    "X_test = pca.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.34742496, 0.19120613])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explain the variance\n",
    "explained_varience = pca.explained_variance_ratio_\n",
    "explained_varience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 3: Applying Logistic Regressiom model after used PCA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the logistic regression model from scikit learn\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the logistic regression model\n",
    "classifier = LogisticRegression(random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the logistic regression model to the training  set\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 4: Making a prediction and visualize the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2, 3, 2, 3, 3, 1, 2, 2, 1, 1, 3, 2, 1, 2, 3, 3, 1, 3, 2, 3,\n",
       "       3, 2, 2, 2, 2, 2, 1, 1, 3, 3, 2, 3, 3, 3], dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making the prediction of test test set result\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0, 14],\n",
       "       [ 2, 13,  1],\n",
       "       [ 6,  0,  0]], dtype=int64)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making a confusion metrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matric = confusion_matrix(y_test, y_pred)\n",
    "confusion_matric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3611111111111111"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accuracy score\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green','blue')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green', 'blue'))(i), label = j)\n",
    "plt.title('Logistic Regression (Test set)')\n",
    "plt.xlabel('PCA 1')\n",
    "plt.ylabel('PCA 2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}