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logisticRegression
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‎logisticRegression/insurance_data.csv

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age,bought_insurance
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22,0
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25,0
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47,1
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52,0
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46,1
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‎logisticRegression/logisticReg(classification problem).ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"outputs": [
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{
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"data": {
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" <th>age</th>\n",
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],
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"text/plain": [
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" age bought_insurance\n",
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"0 22 0\n",
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"1 25 0\n",
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"2 47 1\n",
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"3 52 0\n",
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"4 46 1\n",
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"5 56 1\n",
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"6 55 0\n",
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"7 60 1\n",
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"8 62 1\n",
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"9 61 1\n",
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"10 18 0\n",
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"11 28 0\n",
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"12 27 0\n",
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"13 29 0\n",
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"14 49 1\n",
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"15 55 1\n",
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"16 25 1\n",
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"17 58 1\n",
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"18 19 0\n",
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"19 18 0\n",
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"20 21 0\n",
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"21 26 0\n",
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"22 40 1\n",
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"23 45 1\n",
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"24 50 1\n",
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"25 54 1\n",
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"26 23 0"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline\n",
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"df = pd.read_csv(\"insurance_data.csv\")\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"Xtrain,Xtest,ytrain,ytest = train_test_split(df[['age']],df.bought_insurance,test_size=0.1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.linear_model import LogisticRegression\n",
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"model = LogisticRegression()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
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" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
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" penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
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" verbose=0, warm_start=False)"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model.fit(Xtrain,ytrain)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1])"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model.predict(60) #classify age of 60 will buy insurance or not"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0.63694763, 0.36305237]])"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model.predict_proba(Xtest)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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