diff --git a/How to.odt b/How to.odt
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import the required modules\n",
+    "from sklearn.datasets import make_classification\n",
+    "from matplotlib import pyplot as plt\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-1.04798669]\n",
+      " [-1.18533122]\n",
+      " [ 1.30388268]\n",
+      " [ 0.77921082]\n",
+      " [-0.66270304]\n",
+      " [-0.73517511]\n",
+      " [ 1.0129303 ]\n",
+      " [-0.93736332]\n",
+      " [-1.22869413]\n",
+      " [-1.29906245]\n",
+      " [ 0.84625523]\n",
+      " [-0.44678384]\n",
+      " [-1.27810116]\n",
+      " [-0.81715397]\n",
+      " [-1.3759646 ]\n",
+      " [-1.45907172]\n",
+      " [ 0.86213447]\n",
+      " [ 1.31966782]\n",
+      " [ 0.992724  ]\n",
+      " [-0.59239048]\n",
+      " [-1.04680338]\n",
+      " [-0.09286521]\n",
+      " [ 0.97648125]\n",
+      " [-1.1542901 ]\n",
+      " [ 0.64743536]\n",
+      " [ 0.83918906]\n",
+      " [ 1.2009213 ]\n",
+      " [ 1.10163923]\n",
+      " [-0.55377165]\n",
+      " [ 0.85945976]\n",
+      " [-1.33699615]\n",
+      " [-0.70839315]\n",
+      " [-0.89077893]\n",
+      " [ 1.3955816 ]\n",
+      " [ 0.79877718]\n",
+      " [-1.10126753]\n",
+      " [-0.77791645]\n",
+      " [-0.9837044 ]\n",
+      " [-0.00471974]\n",
+      " [ 0.42358359]\n",
+      " [ 0.97323393]\n",
+      " [ 1.01961803]\n",
+      " [ 0.88678014]\n",
+      " [-0.58178967]\n",
+      " [-1.01645497]\n",
+      " [ 1.01866018]\n",
+      " [ 0.97811732]\n",
+      " [-0.46328955]\n",
+      " [-0.58220468]\n",
+      " [ 1.200104  ]\n",
+      " [ 0.96129761]\n",
+      " [ 1.07521231]\n",
+      " [-1.06676283]\n",
+      " [ 0.66703245]\n",
+      " [-1.48266358]\n",
+      " [-0.95379103]\n",
+      " [-0.50922448]\n",
+      " [ 0.5766528 ]\n",
+      " [-0.68286227]\n",
+      " [-1.07421386]\n",
+      " [-0.79172877]\n",
+      " [-1.32897534]\n",
+      " [ 0.92527711]\n",
+      " [-1.63144075]\n",
+      " [ 0.98152769]\n",
+      " [-0.9277596 ]\n",
+      " [ 0.99513204]\n",
+      " [-1.56576004]\n",
+      " [-1.18322963]\n",
+      " [-1.26981413]\n",
+      " [ 1.17981389]\n",
+      " [ 1.19795714]\n",
+      " [-0.8895733 ]\n",
+      " [ 1.30158202]\n",
+      " [ 0.96883211]\n",
+      " [ 0.75274277]\n",
+      " [-1.02120842]\n",
+      " [-0.90877379]\n",
+      " [-0.6296421 ]\n",
+      " [ 0.86470982]\n",
+      " [-0.8515186 ]\n",
+      " [ 1.16013882]\n",
+      " [-0.75516721]\n",
+      " [-1.32044924]\n",
+      " [ 1.08864952]\n",
+      " [ 1.31526124]\n",
+      " [-0.69391671]\n",
+      " [ 0.93398792]\n",
+      " [-1.42212025]\n",
+      " [-1.16417858]\n",
+      " [-1.21467772]\n",
+      " [-1.27889807]\n",
+      " [-0.3769908 ]\n",
+      " [ 0.91999288]\n",
+      " [-1.56485036]\n",
+      " [-1.01986862]\n",
+      " [ 0.84728703]\n",
+      " [-0.66573826]\n",
+      " [ 0.99244273]\n",
+      " [ 1.15776142]\n",
+      " [ 1.01505624]\n",
+      " [ 0.89444983]\n",
+      " [ 1.50345731]\n",
+      " [-0.99158212]\n",
+      " [ 1.15662115]\n",
+      " [ 0.99783538]\n",
+      " [ 0.86208113]\n",
+      " [-0.55094122]\n",
+      " [ 0.99835555]\n",
+      " [ 0.71681001]\n",
+      " [-1.91372087]\n",
+      " [ 0.76479732]\n",
+      " [ 1.27156396]\n",
+      " [ 1.09839751]\n",
+      " [ 1.00400252]\n",
+      " [-0.75344864]\n",
+      " [ 0.60223506]\n",
+      " [-1.73194991]\n",
+      " [-1.5929839 ]\n",
+      " [-1.28884636]\n",
+      " [ 0.87351535]\n",
+      " [ 0.48517228]\n",
+      " [-1.52216478]\n",
+      " [ 0.69444818]\n",
+      " [ 0.85601014]\n",
+      " [-1.34117394]\n",
+      " [ 1.06720227]\n",
+      " [-1.31004995]\n",
+      " [-0.97309369]\n",
+      " [-0.83737283]\n",
+      " [ 0.82328655]\n",
+      " [-1.36708274]\n",
+      " [ 0.70692957]\n",
+      " [ 1.03636045]\n",
+      " [-0.99336523]\n",
+      " [-0.87996869]\n",
+      " [-1.36451102]\n",
+      " [ 0.68029636]\n",
+      " [-1.41461592]\n",
+      " [ 1.16286916]\n",
+      " [-1.08334534]\n",
+      " [ 0.85772781]\n",
+      " [-1.11696295]\n",
+      " [ 0.87627224]\n",
+      " [ 0.95710966]\n",
+      " [ 0.75226261]\n",
+      " [ 0.69270705]\n",
+      " [-1.7941007 ]\n",
+      " [ 0.90707982]\n",
+      " [ 0.88402826]\n",
+      " [-1.01107845]\n",
+      " [-0.29958654]\n",
+      " [-1.13859804]\n",
+      " [ 1.07290754]\n",
+      " [ 0.60718351]\n",
+      " [ 1.09774186]\n",
+      " [ 0.83100714]\n",
+      " [-1.96633847]\n",
+      " [-0.98418513]\n",
+      " [-0.9663529 ]\n",
+      " [ 1.1207593 ]\n",
+      " [ 1.52081546]\n",
+      " [ 1.04894859]\n",
+      " [ 0.79643643]\n",
+      " [ 0.92910662]\n",
+      " [ 0.91140536]\n",
+      " [-0.46227849]\n",
+      " [ 0.88936215]\n",
+      " [ 0.93760681]\n",
+      " [ 0.83783787]\n",
+      " [-1.86302163]\n",
+      " [-1.1519962 ]\n",
+      " [-1.27718473]\n",
+      " [ 0.77842533]\n",
+      " [ 1.04897324]\n",
+      " [ 0.89562746]\n",
+      " [-1.52215334]\n",
+      " [ 0.72946825]\n",
+      " [ 1.0339013 ]\n",
+      " [-1.30609303]\n",
+      " [-1.19891745]\n",
+      " [-0.77372154]\n",
+      " [-1.21857242]\n",
+      " [-0.57381702]\n",
+      " [-0.92105519]\n",
+      " [-1.56994703]\n",
+      " [ 0.83183766]\n",
+      " [ 0.9388098 ]\n",
+      " [-0.92321381]\n",
+      " [ 0.95491747]\n",
+      " [ 0.97491984]\n",
+      " [ 1.27302445]\n",
+      " [ 0.86112564]\n",
+      " [-1.12353232]\n",
+      " [-0.53593672]\n",
+      " [ 1.37928873]\n",
+      " [-0.771236  ]\n",
+      " [ 0.94489733]\n",
+      " [-1.01449996]\n",
+      " [ 0.95553333]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 1 1 1 1 0 1 0 0 0 1 1 0 0\n",
+      " 0 0 0 1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 1 0 1\n",
+      " 1 1 0 0 0 1 0 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0\n",
+      " 1 1 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 1 1 1 1 0\n",
+      " 1 1 0 0 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 0 1 1 0 0 0 0 0 0\n",
+      " 0 1 1 0 1 1 1 1 0 0 1 0 1 0 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Generate the data set\n",
+    "x,y = make_classification(n_samples=200,\n",
+    "                         n_features=1,\n",
+    "                         n_classes=2,\n",
+    "                         n_clusters_per_class=1,\n",
+    "                         flip_y=0.03,\n",
+    "                          n_informative=1,\n",
+    "                          n_redundant=0,\n",
+    "                          n_repeated=0 \n",
+    "                         )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Visualize using scatter plot\n",
+    "plt.scatter(x,y,cmap='Reds')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Split dataset into training and test data set\n",
+    "x_train, x_test, y_train,  y_test=train_test_split(x,y, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Create the Logistic Regression Object\n",
+    "Ir = LogisticRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Perform Logistic Regression\n",
+    "Ir.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3.3859551]]\n",
+      "[-0.47024533]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Observe the coefficient and intercept\n",
+    "print(Ir.coef_)\n",
+    "print(Ir.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#make prediction at this point\n",
+    "y_pred = Ir.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[30,  1],\n",
+       "       [ 1, 18]], dtype=int64)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Display the confusion matrix\n",
+    "confusion_matrix(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}