logistic-regression-classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification template\n",
    "\n",
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow==1.13.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from -r requirements.txt (line 1)) (1.13.1)\n",
      "Requirement already satisfied: numpy==1.16.2 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from -r requirements.txt (line 2)) (1.16.2)\n",
      "Requirement already satisfied: pandas==0.24.2 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from -r requirements.txt (line 3)) (0.24.2)\n",
      "Requirement already satisfied: matplotlib==3.0.3 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from -r requirements.txt (line 4)) (3.0.3)\n",
      "Requirement already satisfied: Keras==2.2.4 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from -r requirements.txt (line 5)) (2.2.4)\n",
      "Requirement already satisfied: sklearn in /srv/conda/envs/notebook/lib/python3.7/site-packages (from -r requirements.txt (line 6)) (0.0)\n",
      "Requirement already satisfied: tensorboard<1.14.0,>=1.13.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.13.1)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.1.0)\n",
      "Requirement already satisfied: six>=1.10.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.12.0)\n",
      "Requirement already satisfied: tensorflow-estimator<1.14.0rc0,>=1.13.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.13.0)\n",
      "Requirement already satisfied: astor>=0.6.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (0.8.0)\n",
      "Requirement already satisfied: absl-py>=0.1.6 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (0.7.1)\n",
      "Requirement already satisfied: gast>=0.2.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (0.2.2)\n",
      "Requirement already satisfied: keras-applications>=1.0.6 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.0.8)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.1.0)\n",
      "Requirement already satisfied: wheel>=0.26 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (0.33.4)\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.21.1)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (3.8.0)\n",
      "Requirement already satisfied: pytz>=2011k in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pandas==0.24.2->-r requirements.txt (line 3)) (2019.1)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pandas==0.24.2->-r requirements.txt (line 3)) (2.8.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib==3.0.3->-r requirements.txt (line 4)) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib==3.0.3->-r requirements.txt (line 4)) (1.1.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib==3.0.3->-r requirements.txt (line 4)) (2.4.0)\n",
      "Requirement already satisfied: h5py in /srv/conda/envs/notebook/lib/python3.7/site-packages (from Keras==2.2.4->-r requirements.txt (line 5)) (2.9.0)\n",
      "Requirement already satisfied: pyyaml in /srv/conda/envs/notebook/lib/python3.7/site-packages (from Keras==2.2.4->-r requirements.txt (line 5)) (5.1.1)\n",
      "Requirement already satisfied: scipy>=0.14 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from Keras==2.2.4->-r requirements.txt (line 5)) (1.3.0)\n",
      "Requirement already satisfied: scikit-learn in /srv/conda/envs/notebook/lib/python3.7/site-packages (from sklearn->-r requirements.txt (line 6)) (0.21.2)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.1->-r requirements.txt (line 1)) (0.15.4)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.1->-r requirements.txt (line 1)) (3.1.1)\n",
      "Requirement already satisfied: mock>=2.0.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow==1.13.1->-r requirements.txt (line 1)) (3.0.5)\n",
      "Requirement already satisfied: setuptools in /srv/conda/envs/notebook/lib/python3.7/site-packages (from protobuf>=3.6.1->tensorflow==1.13.1->-r requirements.txt (line 1)) (41.0.1)\n",
      "Requirement already satisfied: joblib>=0.11 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from scikit-learn->sklearn->-r requirements.txt (line 6)) (0.13.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing\n",
    "### Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, [2, 3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting classifier to the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "classifier = PassiveAggressiveClassifier(random_state = 0)\n",
    "classifier.fit(X_train,y_train)\n",
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making the Confusion Matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[53 15]\n",
      " [ 1 31]]\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising\n",
    "\n",
    "## Training set results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\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(('pink', 'lightgreen')))\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'))(i), label = j)\n",
    "plt.title('Classifier (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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(('pink', 'lightgreen')))\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'))(i), label = j)\n",
    "plt.title('Classifier (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Awesome!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Credits\n",
    "based on superdatascience.com Machine Learning A-Z, by Kirill Eremenko and Hadelin de Ponteves. Thanks For sharing the knowleged!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}