Logistic regression using scikit-learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is Basic explination of Logistic Regression for Machine Learning. The Dataset i have used is called iris dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I have used this dataset from scikit's own package of datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The columns in this dataset are:\n",
    "\n",
    "\n",
    "-  SepalLengthCm <br><br>\n",
    "-  SepalWidthCm <br><br>\n",
    "-  PetalLengthCm <br><br>\n",
    "-  PetalWidthCm <br><br>\n",
    "-  Species <br><br>\n",
    "\n",
    "in which the four colum are lables and last one is target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# other dependencies that you might not need\n",
    "# just for publishing image in notebook\n",
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data\n",
    "data = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here as we have imported the data through sckit's package , we will get the data in form of arrays,\n",
    "# so here our labes is stored in \n",
    "data.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and labels\n",
    "data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First let's divide the dataset into data and target \n",
    "\n",
    "X = data.data[:, :2]   # X is the features in our dataset\n",
    "y = (data.target != 0) * 1  # y is the Labels in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for ease let's visualise the data\n",
    "# here we have only taken the x line as SepalLengthCm and y line as SepalWidthCm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color='b', label='0') \n",
    "plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='r', label='1')\n",
    "plt.legend();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dependencies\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load model\n",
    "model = LogisticRegression(C=1e20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1e+20, 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=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit model\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict Model\n",
    "preds = model.predict(X)\n",
    "(preds == y).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hence we got all the values correct for our predicted dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "## Further You can divide the dataset using train_test_split in scikit-learn package and then train it with training data and testing with test data.\n",
    "\n",
    "### This will give the accuracy score of the model when it will get an unknown data as input as in this example i have used the same dataset for training and then for testing, so it gives 100% accuracy.\n"
   ]
  },
  {
   "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",
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 },
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