diff --git a/Logistic Regression code.ipynb b/Logistic Regression code.ipynb new file mode 100644 index 0000000..b79568e --- /dev/null +++ b/Logistic Regression code.ipynb @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### In the field of machine learning, it's the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to. \n", + "\n", + "- Logistic regression is the means by which classification is made based on predictor function that is linear, combining weights with the values of the dependent variable which in this case is our x\n", + "\n", + "#### Note: This is only for a binary classification problem not multi-classification" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "#TO remove depreciation warnings when we run the code\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "warnings.filterwarnings('ignore', category=DeprecationWarning)\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class logistic_regression:\n", + " \n", + " def __init__(self,iterations,alpha):\n", + " self.iterations=iterations #choosing the number of iterations (Hyperparameter)\n", + " self.alpha=alpha #choosing alpha(Hyperparameter) \n", + " \n", + " def sigmoid(self,z):\n", + " return(1/(1+np.exp(-z))) #sigmoid function\n", + " \n", + " def fit(self,x,y): #(X-data for training, y - Output) \n", + " m=x.shape[0] \n", + " self.w=np.random.randn(x.shape[1],1) #Initializing the weight\n", + " \n", + " cost_vals=[] \n", + " for i in range(2): #For each number of iterations\n", + " a= np.dot(x,self.w) #multiplying the weights with the Feature values and summing them up\n", + " z=self.sigmoid(a) #Using link function to transform the data\n", + " \n", + " cost = (-1/m) *( np.dot(y,np.log(z))+(np.dot((1-y),np.log(1-z)))) #Calculating the cost function\n", + " \n", + " cost_vals.append(cost) #Creating a list with all cost values for each iteration\n", + " \n", + " dw = np.dot(x.T,z-np.array([y])).mean() #Calculating the gradient\n", + " \n", + " self.w=self.w-(self.alpha*dw) #updating the weights\n", + " return self\n", + " \n", + " def predict(self,x,threshold): \n", + " probability=self.sigmoid(np.dot(x,self.w)) #predicting a new set of values based on the training \n", + "\n", + " if(probability>threshold):\n", + " return (1)\n", + " else:\n", + " return (0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('pimadiabetics.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(768, 9)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Number of times pregnant Plasma glucose concentration \\\n", + "0 6 148 \n", + "1 1 85 \n", + "2 8 183 \n", + "3 1 89 \n", + "4 0 137 \n", + "\n", + " Diastolic blood pressure Triceps skinfold thickness 2-Hour serum insulin \\\n", + "0 72 35 0 \n", + "1 66 29 0 \n", + "2 64 0 0 \n", + "3 66 23 94 \n", + "4 40 35 168 \n", + "\n", + " Body mass index Diabetes pedigree function Age Class \n", + "0 33.6 0.627 50 1 \n", + "1 26.6 0.351 31 0 \n", + "2 23.3 0.672 32 1 \n", + "3 28.1 0.167 21 0 \n", + "4 43.1 2.288 33 1 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class_0 = np.where(df['Class']==1)\n", + "class_1 = np.where(df['Class']==0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax1=plt.subplots(1,1,figsize=(10,5))\n", + "ax1.scatter(df.iloc[class_1]['Age'],df.iloc[class_1]['Class'],s=50,c='b',marker='+',label='One class')\n", + "ax1.scatter(df.iloc[class_0]['Age'],df.iloc[class_0]['Class'],s=50,c='g',marker='o',label=\"Second Class\")\n", + "plt.hlines(y=0.5,xmin=100,xmax=500)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#Initialize the class\n", + "obj=logistic_regression(df.shape[0],0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#Convert the features to a matrix\n", + "X = df.iloc[:,0:8].values\n", + "y = df['Class'].values" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "model = obj.fit(X,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict([[1,115,70,30,96,34.6,0.529,32]],0.5)" + ] + } + ], + "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": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/model.py b/model.py new file mode 100644 index 0000000..9ca7f74 --- /dev/null +++ b/model.py @@ -0,0 +1,40 @@ +import numpy as np + +class logistic_regression: + + def __init__(self,iterations,alpha): + self.iterations=iterations #choosing the number of iterations (Hyperparameter) + self.alpha=alpha #choosing alpha(Hyperparameter) + + def sigmoid(self,z): + return(1/(1+np.exp(-z))) #sigmoid function + + def fit(self,x,y): #(X-data for training, y - Output) + m=x.shape[0] + self.w=np.random.randn(x.shape[1],1) #Initializing the weight + + cost_vals=[] + for i in range(2): #For each number of iterations + a= np.dot(x,self.w) #multiplying the weights with the Feature values and summing them up + z=self.sigmoid(a) #Using link function to transform the data + + cost = (-1/m) *( np.dot(y,np.log(z))+(np.dot((1-y),np.log(1-z)))) #Calculating the cost function + + cost_vals.append(cost) #Creating a list with all cost values for each iteration + + dw = np.dot(x.T,z-np.array([y])).mean() #Calculating the gradient + + self.w=self.w-(self.alpha*dw) #updating the weights + return self + + def predict(self,x,threshold): + probability=self.sigmoid(np.dot(x,self.w)) #predicting a new set of values based on the training + + if(probability>threshold): + return (1) + else: + return (0) + + +if __name__ == "__main__": + logistic_regression() \ No newline at end of file diff --git a/pimadiabetics.csv b/pimadiabetics.csv new file mode 100644 index 0000000..f1ca0b8 --- /dev/null +++ b/pimadiabetics.csv @@ -0,0 +1,769 @@ +Number of times pregnant,Plasma glucose concentration,Diastolic blood pressure,Triceps skinfold thickness,2-Hour serum insulin,Body mass index,Diabetes pedigree function,Age,Class +6,148,72,35,0,33.6,0.627,50,1 +1,85,66,29,0,26.6,0.351,31,0 +8,183,64,0,0,23.3,0.672,32,1 +1,89,66,23,94,28.1,0.167,21,0 +0,137,40,35,168,43.1,2.288,33,1 +5,116,74,0,0,25.6,0.201,30,0 +3,78,50,32,88,31.0,0.248,26,1 +10,115,0,0,0,35.3,0.134,29,0 +2,197,70,45,543,30.5,0.158,53,1 +8,125,96,0,0,0.0,0.232,54,1 +4,110,92,0,0,37.6,0.191,30,0 +10,168,74,0,0,38.0,0.537,34,1 +10,139,80,0,0,27.1,1.441,57,0 +1,189,60,23,846,30.1,0.398,59,1 +5,166,72,19,175,25.8,0.587,51,1 +7,100,0,0,0,30.0,0.484,32,1 +0,118,84,47,230,45.8,0.551,31,1 +7,107,74,0,0,29.6,0.254,31,1 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