diff --git a/.ipynb_checkpoints/basic-regression-checkpoint.ipynb b/.ipynb_checkpoints/basic-regression-checkpoint.ipynb new file mode 100644 index 0000000..2fd6442 --- /dev/null +++ b/.ipynb_checkpoints/basic-regression-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/50_Startups.csv b/50_Startups.csv new file mode 100644 index 0000000..55ce26e --- /dev/null +++ b/50_Startups.csv @@ -0,0 +1 @@ +Welcome To Zscaler Directory Authentication
Please wait a moment while we launch our security service.
\ No newline at end of file diff --git a/Position_Salaries.csv b/Position_Salaries.csv new file mode 100644 index 0000000..b4f4776 --- /dev/null +++ b/Position_Salaries.csv @@ -0,0 +1 @@ +Welcome To Zscaler Directory Authentication
Please wait a moment while we launch our security service.
\ No newline at end of file diff --git a/Salary_Data.csv b/Salary_Data.csv new file mode 100644 index 0000000..3dc1120 --- /dev/null +++ b/Salary_Data.csv @@ -0,0 +1 @@ +Welcome To Zscaler Directory Authentication
Please wait a moment while we launch our security service.
\ No newline at end of file diff --git a/balance.txt b/balance.txt new file mode 100644 index 0000000..2ba4c55 --- /dev/null +++ b/balance.txt @@ -0,0 +1 @@ +Welcome To Zscaler Directory Authentication
Please wait a moment while we launch our security service.
\ No newline at end of file diff --git a/basic-regression.ipynb b/basic-regression.ipynb new file mode 100644 index 0000000..d009d5a --- /dev/null +++ b/basic-regression.ipynb @@ -0,0 +1,479 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import r2_score\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "X = np.linspace(1, 20, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1. , 1.19191919, 1.38383838, 1.57575758, 1.76767677])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# y = f(x) = 5x + 20\n", + "y = [np.random.normal(5,1)*n + 20 for n in X]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[26.621248367075914,\n", + " 25.13998936015423,\n", + " 25.969611911161454,\n", + " 27.616635303795668,\n", + " 30.58723548222437]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "X = sm.add_constant(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 2)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1. , 1. ],\n", + " [1. , 1.19191919],\n", + " [1. , 1.38383838],\n", + " [1. , 1.57575758],\n", + " [1. , 1.76767677]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(67, 2)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(33, 2)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: y R-squared: 0.804
Model: OLS Adj. R-squared: 0.801
Method: Least Squares F-statistic: 266.2
Date: Wed, 18 Jul 2018 Prob (F-statistic): 1.14e-24
Time: 13:15:08 Log-Likelihood: -261.71
No. Observations: 67 AIC: 527.4
Df Residuals: 65 BIC: 531.8
Df Model: 1
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
const 23.4982 3.257 7.214 0.000 16.993 30.003
x1 4.3932 0.269 16.316 0.000 3.855 4.931
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Omnibus: 0.086 Durbin-Watson: 1.756
Prob(Omnibus): 0.958 Jarque-Bera (JB): 0.015
Skew: 0.026 Prob(JB): 0.992
Kurtosis: 2.948 Cond. No. 26.6


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.804\n", + "Model: OLS Adj. R-squared: 0.801\n", + "Method: Least Squares F-statistic: 266.2\n", + "Date: Wed, 18 Jul 2018 Prob (F-statistic): 1.14e-24\n", + "Time: 13:15:08 Log-Likelihood: -261.71\n", + "No. Observations: 67 AIC: 527.4\n", + "Df Residuals: 65 BIC: 531.8\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const 23.4982 3.257 7.214 0.000 16.993 30.003\n", + "x1 4.3932 0.269 16.316 0.000 3.855 4.931\n", + "==============================================================================\n", + "Omnibus: 0.086 Durbin-Watson: 1.756\n", + "Prob(Omnibus): 0.958 Jarque-Bera (JB): 0.015\n", + "Skew: 0.026 Prob(JB): 0.992\n", + "Kurtosis: 2.948 Cond. No. 26.6\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = sm.OLS(y_train, X_train)\n", + "results = model.fit()\n", + "results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([23.49822797, 4.393224 ])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.params" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([97.87240401, 72.578084 , 86.91153201, 65.832932 , 64.98978799])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Predicted results\n", + "y_hat =results.predict(X_test)\n", + "y_hat[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[74.54317734493611,\n", + " 63.00724166955317,\n", + " 85.36085343868754,\n", + " 59.446255183341584,\n", + " 75.93961676217643]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# actual results\n", + "y_test[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8557832917117888" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r2_score(y_test, y_hat)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'actual vs predicted')" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_test[:,1], y_test, label ='actual')\n", + "plt.scatter(X_test[:,1], y_hat, label ='predicted')\n", + "plt.legend()\n", + "plt.legend()\n", + "plt.title('actual vs predicted')" + ] + }, + { + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/cars_multivariate.txt b/cars_multivariate.txt new file mode 100644 index 0000000..702faab --- /dev/null +++ b/cars_multivariate.txt @@ -0,0 +1 @@ +Welcome To Zscaler Directory Authentication
Please wait a moment while we launch our security service.
\ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000..e69de29 diff --git a/tc_bmi.csv b/tc_bmi.csv new file mode 100644 index 0000000..607fe0f --- /dev/null +++ b/tc_bmi.csv @@ -0,0 +1 @@ +Welcome To Zscaler Directory Authentication
Please wait a moment while we launch our security service.
\ No newline at end of file