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a/parthpathak1998@gmail.com/Project/Parth Pathak Assignment - AITS.ipynb b/parthpathak1998@gmail.com/Project/Parth Pathak Assignment - AITS.ipynb new file mode 100644 index 000000000..732ddfa87 --- /dev/null +++ b/parthpathak1998@gmail.com/Project/Parth Pathak Assignment - AITS.ipynb @@ -0,0 +1,1472 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Internship Assignment - Write a report comparing 5 classification algorithims." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## AI.Tech Systems https://ai-techsystems.com/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import pandas as pd " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(\"MM.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(830, 6)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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BI-RADSAgeShapeMarginDensitySeverity
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" + ], + "text/plain": [ + " BI-RADS Age Shape Margin Density Severity\n", + "0 5 67 3 5 3 1\n", + "1 5 58 4 5 3 1\n", + "2 4 28 1 1 3 0\n", + "3 5 57 1 5 3 1\n", + "4 5 76 1 4 3 1" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "BI-RADS int64\n", + "Age int64\n", + "Shape int64\n", + "Margin int64\n", + "Density int64\n", + "Severity int64\n", + "dtype: object" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "427 403\n" + ] + } + ], + "source": [ + "x0 =data.Severity.value_counts()[0]\n", + "x1 = data.Severity.value_counts()[1]\n", + "print(x0 , x1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels = \"Benign\" , \"Malingant\"\n", + "sizes = [x0 , x1]\n", + "explode = [0,0.3]\n", + "plt.pie(sizes , explode=explode , labels=labels , shadow=True , startangle=90 , autopct='%1.1f%%')\n", + "plt.axis(\"equal\")\n", + "plt.title(\"Freq of Diagnosis\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = data.loc[data[\"Severity\"]== 0]\n", + "y_Val1 = data1.Age.values.reshape(-1,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data.loc[data[\"Severity\"]== 1]\n", + "y_Val2 = data1.Age.values.reshape(-1,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "x = data.iloc[:,0:5]\n", + "y = data.iloc[:,5]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "X_TRAIN , X_TEST , Y_TRAIN , Y_TEST = train_test_split(x, y, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(664, 5)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_TRAIN.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(166, 5)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_TEST.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PRE PROCESSING OF DATA" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import preprocessing\n", + "scaler = preprocessing.StandardScaler()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "F:\\Anaconda\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n", + " return self.partial_fit(X, y)\n", + "F:\\Anaconda\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n", + " return self.fit(X, **fit_params).transform(X)\n", + "F:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n", + " \n" + ] + } + ], + "source": [ + "x_train = scaler.fit_transform(X_TRAIN)\n", + "x_test = scaler.transform(X_TEST)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = pca.fit_transform(x_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "X_test = pca.transform(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total = sum(pca.explained_variance_)\n", + "k = 0\n", + "cur_var = 0 \n", + "while cur_var / total < 0.999:\n", + " cur_var += pca.explained_variance_[k]\n", + " k = k+1\n", + "k" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "pca = PCA(n_components=k)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "x_train_pca = pca.fit_transform(x_train)\n", + "x_test_pca = pca.transform(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SVM" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import svm" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "clf1 = svm.SVC(kernel='rbf')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf1.fit(x_train_pca,Y_TRAIN)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "pred2 = clf1.predict(x_test_pca)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7951807228915663\n" + ] + } + ], + "source": [ + "print(clf1.score(x_test_pca,Y_TEST))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[65, 14],\n", + " [20, 67]], dtype=int64)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion_matrix(Y_TEST, pred2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DEC TREE" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "clf = DecisionTreeClassifier()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", + " splitter='best')" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf.fit(x_train_pca,Y_TRAIN)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "pred3 = clf.predict(x_test_pca)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9518072289156626\n", + "0.7469879518072289\n" + ] + } + ], + "source": [ + "print(clf.score(x_train_pca,Y_TRAIN))\n", + "print(clf.score(x_test_pca,Y_TEST))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[63, 16],\n", + " [26, 61]], dtype=int64)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion_matrix(Y_TEST, pred3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RANDOM FORR" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "F:\\Anaconda\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", + " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.9412650602409639, 0.7951807228915663)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "clf = RandomForestClassifier()\n", + "clf.fit(x_train_pca, Y_TRAIN)\n", + "clf.score(x_train_pca,Y_TRAIN),clf.score(x_test_pca,Y_TEST)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "pred4 = clf.predict(x_test_pca)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[66, 13],\n", + " [21, 66]], dtype=int64)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion_matrix(Y_TEST , pred4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Boosted Trees" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7831325301204819\n", + "0.911144578313253\n" + ] + } + ], + "source": [ + "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,max_depth=1, random_state=0).fit(x_train_pca, Y_TRAIN)\n", + "print(clf.score(x_test_pca, Y_TEST)) \n", + "print(clf.score(x_train_pca , Y_TRAIN))" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "pred_6 = clf.predict(x_test_pca)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[65, 14],\n", + " [22, 65]], dtype=int64)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "confusion_matrix(Y_TEST , pred_6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Artificial Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "X = data.iloc[:, 0:5].values" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "y = data.iloc[:, 5].values" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 67, 3, 5, 3],\n", + " [ 5, 58, 4, 5, 3],\n", + " [ 4, 28, 1, 1, 3],\n", + " ...,\n", + " [ 4, 64, 4, 5, 3],\n", + " [ 5, 66, 4, 5, 3],\n", + " [ 4, 62, 3, 3, 3]], dtype=int64)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0,\n", + " 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0,\n", + " 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0,\n", + " 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n", + " 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,\n", + " 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0,\n", + " 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,\n", + " 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n", + " 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n", + " 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1,\n", + " 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1,\n", + " 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0,\n", + " 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1,\n", + " 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n", + " 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n", + " 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n", + " 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0,\n", + " 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n", + " 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n", + " 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1,\n", + " 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n", + " 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0,\n", + " 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0,\n", + " 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n", + " 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n", + " 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0,\n", + " 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n", + " 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n", + " 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n", + " 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,\n", + " 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1,\n", + " 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n", + " 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,\n", + " 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n", + " 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1,\n", + " 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1,\n", + " 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0], dtype=int64)" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "sc = StandardScaler()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "F:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", + " warnings.warn(msg, DataConversionWarning)\n", + "F:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", + " warnings.warn(msg, DataConversionWarning)\n" + ] + } + ], + "source": [ + "X_train = sc.fit_transform(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "F:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", + " warnings.warn(msg, DataConversionWarning)\n" + ] + } + ], + "source": [ + "X_test = sc.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "F:\\Anaconda\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n", + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import keras" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.models import Sequential" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.layers import Dense" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "classifier = Sequential()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "classifier.add?" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "Dense?" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "classifier.add(Dense(units = 3, kernel_initializer='glorot_uniform', activation='relu', input_dim=5))" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "classifier.add(Dense(units = 3, kernel_initializer='glorot_uniform', activation='relu'))" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "classifier.add(Dense(units = 1, kernel_initializer='glorot_uniform', activation='sigmoid'))" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "classifier.compile?" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "F:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "664/664 [==============================] - 1s 2ms/step - loss: 0.6831 - acc: 0.5873\n", + "Epoch 2/100\n", + "664/664 [==============================] - 0s 209us/step - loss: 0.6425 - acc: 0.6883\n", + "Epoch 3/100\n", + "664/664 [==============================] - 0s 207us/step - loss: 0.6059 - acc: 0.7410\n", + "Epoch 4/100\n", + "664/664 [==============================] - 0s 206us/step - loss: 0.5807 - acc: 0.7651\n", + "Epoch 5/100\n", + "664/664 [==============================] - 0s 207us/step - loss: 0.5608 - acc: 0.7816\n", + "Epoch 6/100\n", + "664/664 [==============================] - 0s 209us/step - loss: 0.5455 - acc: 0.7816\n", + "Epoch 7/100\n", + "664/664 [==============================] - 0s 212us/step - loss: 0.5325 - acc: 0.7877\n", + "Epoch 8/100\n", + "664/664 [==============================] - 0s 225us/step - loss: 0.5186 - acc: 0.7937\n", + "Epoch 9/100\n", + "664/664 [==============================] - 0s 207us/step - loss: 0.5045 - acc: 0.7967\n", + "Epoch 10/100\n", + "664/664 [==============================] - 0s 216us/step - loss: 0.4912 - acc: 0.8117\n", + "Epoch 11/100\n", + "664/664 [==============================] - 0s 227us/step - loss: 0.4789 - acc: 0.8163\n", + "Epoch 12/100\n", + "664/664 [==============================] - 0s 221us/step - loss: 0.4680 - acc: 0.8133\n", + "Epoch 13/100\n", + "664/664 [==============================] - 0s 219us/step - loss: 0.4572 - acc: 0.8148\n", + "Epoch 14/100\n", + "664/664 [==============================] - 0s 228us/step - loss: 0.4489 - acc: 0.8178\n", + "Epoch 15/100\n", + "664/664 [==============================] - 0s 231us/step - loss: 0.4409 - acc: 0.8193\n", + "Epoch 16/100\n", + "664/664 [==============================] - 0s 224us/step - loss: 0.4333 - acc: 0.8193\n", + "Epoch 17/100\n", + "664/664 [==============================] - 0s 218us/step - loss: 0.4273 - acc: 0.8208\n", + "Epoch 18/100\n", + "664/664 [==============================] - 0s 216us/step - loss: 0.4230 - acc: 0.8163\n", + "Epoch 19/100\n", + "664/664 [==============================] - 0s 233us/step - loss: 0.4191 - acc: 0.8223\n", + "Epoch 20/100\n", + "664/664 [==============================] - 0s 279us/step - loss: 0.4157 - acc: 0.8193\n", + "Epoch 21/100\n", + "664/664 [==============================] - 0s 270us/step - loss: 0.4127 - acc: 0.8238\n", + "Epoch 22/100\n", + "664/664 [==============================] - 0s 284us/step - loss: 0.4100 - acc: 0.8223\n", + "Epoch 23/100\n", + "664/664 [==============================] - 0s 218us/step - loss: 0.4077 - acc: 0.8268\n", + "Epoch 24/100\n", + "664/664 [==============================] - 0s 224us/step - loss: 0.4054 - acc: 0.8283\n", + "Epoch 25/100\n", + "664/664 [==============================] - 0s 225us/step - loss: 0.4031 - acc: 0.8298\n", + "Epoch 26/100\n", + "664/664 [==============================] - 0s 225us/step - loss: 0.4010 - acc: 0.8298\n", + "Epoch 27/100\n", + "664/664 [==============================] - 0s 231us/step - loss: 0.3991 - acc: 0.8313\n", + "Epoch 28/100\n", + "664/664 [==============================] - 0s 216us/step - loss: 0.3971 - acc: 0.8313\n", + "Epoch 29/100\n", + "664/664 [==============================] - 0s 233us/step - loss: 0.3951 - acc: 0.8313\n", + "Epoch 30/100\n", + "664/664 [==============================] - 0s 218us/step - loss: 0.3942 - acc: 0.8328\n", + "Epoch 31/100\n", + "664/664 [==============================] - 0s 287us/step - loss: 0.3920 - acc: 0.8343\n", + "Epoch 32/100\n", + "664/664 [==============================] - 0s 270us/step - loss: 0.3904 - acc: 0.8389\n", + "Epoch 33/100\n", + "664/664 [==============================] - 0s 257us/step - loss: 0.3896 - acc: 0.8389\n", + "Epoch 34/100\n", + "664/664 [==============================] - 0s 261us/step - loss: 0.3877 - acc: 0.8404\n", + "Epoch 35/100\n", + "664/664 [==============================] - 0s 264us/step - loss: 0.3860 - acc: 0.8358\n", + "Epoch 36/100\n", + "664/664 [==============================] - 0s 251us/step - loss: 0.3835 - acc: 0.8328\n", + "Epoch 37/100\n", + "664/664 [==============================] - 0s 242us/step - loss: 0.3821 - acc: 0.8343\n", + "Epoch 38/100\n", + "664/664 [==============================] - 0s 216us/step - loss: 0.3801 - acc: 0.8404\n", + "Epoch 39/100\n", + "664/664 [==============================] - 0s 225us/step - loss: 0.3796 - acc: 0.8389\n", + "Epoch 40/100\n", + "664/664 [==============================] - 0s 215us/step - loss: 0.3776 - acc: 0.8389\n", + "Epoch 41/100\n", + "664/664 [==============================] - 0s 221us/step - loss: 0.3768 - acc: 0.8373\n", + "Epoch 42/100\n", + "664/664 [==============================] - 0s 221us/step - loss: 0.3756 - acc: 0.8328\n", + "Epoch 43/100\n", + "664/664 [==============================] - 0s 215us/step - loss: 0.3744 - acc: 0.8389\n", + "Epoch 44/100\n", + "664/664 [==============================] - 0s 218us/step - loss: 0.3740 - acc: 0.8373\n", + "Epoch 45/100\n", + "664/664 [==============================] - 0s 221us/step - loss: 0.3730 - acc: 0.8373\n", + "Epoch 46/100\n", + "664/664 [==============================] - 0s 224us/step - loss: 0.3726 - acc: 0.8373\n", + "Epoch 47/100\n", + "664/664 [==============================] - 0s 221us/step - loss: 0.3724 - acc: 0.8373\n", + "Epoch 48/100\n", + "664/664 [==============================] - 0s 215us/step - loss: 0.3714 - acc: 0.8373\n", + "Epoch 49/100\n", + "664/664 [==============================] - 0s 222us/step - loss: 0.3712 - acc: 0.8343\n", + "Epoch 50/100\n", + "664/664 [==============================] - 0s 224us/step - loss: 0.3710 - acc: 0.8373\n", + "Epoch 51/100\n", + "664/664 [==============================] - 0s 228us/step - loss: 0.3705 - acc: 0.8373\n", + "Epoch 52/100\n", + "664/664 [==============================] - 0s 213us/step - loss: 0.3705 - acc: 0.8404\n", + "Epoch 53/100\n", + "664/664 [==============================] - 0s 212us/step - loss: 0.3699 - acc: 0.8358\n", + "Epoch 54/100\n", + "664/664 [==============================] - 0s 218us/step - loss: 0.3697 - acc: 0.8373\n", + "Epoch 55/100\n", + "664/664 [==============================] - 0s 216us/step - loss: 0.3690 - acc: 0.8373\n", + "Epoch 56/100\n", + "664/664 [==============================] - 0s 204us/step - loss: 0.3689 - acc: 0.8389\n", + "Epoch 57/100\n", + "664/664 [==============================] - 0s 216us/step - loss: 0.3683 - acc: 0.8389\n", + "Epoch 58/100\n", + "664/664 [==============================] - 0s 213us/step - loss: 0.3686 - acc: 0.8434\n", + "Epoch 59/100\n", + "664/664 [==============================] - 0s 206us/step - loss: 0.3680 - acc: 0.8419\n", + "Epoch 60/100\n", + "664/664 [==============================] - 0s 227us/step - loss: 0.3688 - acc: 0.8389\n", + "Epoch 61/100\n", + "664/664 [==============================] - 0s 219us/step - loss: 0.3675 - acc: 0.8419\n", + "Epoch 62/100\n", + "664/664 [==============================] - 0s 228us/step - loss: 0.3678 - acc: 0.8449\n", + "Epoch 63/100\n", + "664/664 [==============================] - 0s 239us/step - loss: 0.3672 - acc: 0.8419\n", + "Epoch 64/100\n", + "664/664 [==============================] - 0s 219us/step - loss: 0.3678 - acc: 0.8404\n", + "Epoch 65/100\n", + "664/664 [==============================] - 0s 278us/step - loss: 0.3672 - acc: 0.8509\n", + "Epoch 66/100\n", + "664/664 [==============================] - 0s 278us/step - loss: 0.3673 - acc: 0.8464\n", + "Epoch 67/100\n", + "664/664 [==============================] - 0s 249us/step - loss: 0.3669 - acc: 0.8464\n", + "Epoch 68/100\n", + "664/664 [==============================] - 0s 249us/step - loss: 0.3670 - acc: 0.8419\n", + "Epoch 69/100\n", + "664/664 [==============================] - 0s 249us/step - loss: 0.3664 - acc: 0.8479\n", + "Epoch 70/100\n", + "664/664 [==============================] - 0s 269us/step - loss: 0.3669 - acc: 0.8464\n", + "Epoch 71/100\n", + "664/664 [==============================] - 0s 243us/step - loss: 0.3661 - acc: 0.8464\n", + "Epoch 72/100\n", + "664/664 [==============================] - 0s 231us/step - loss: 0.3662 - acc: 0.8449\n", + "Epoch 73/100\n", + "664/664 [==============================] - 0s 218us/step - loss: 0.3663 - acc: 0.8434\n", + "Epoch 74/100\n", + "664/664 [==============================] - 0s 227us/step - loss: 0.3666 - acc: 0.8464\n", + "Epoch 75/100\n", + "664/664 [==============================] - 0s 221us/step - loss: 0.3659 - acc: 0.8464 0s - loss: 0.3574 - acc: 0.846\n", + "Epoch 76/100\n", + "664/664 [==============================] - 0s 242us/step - loss: 0.3655 - acc: 0.8464\n", + "Epoch 77/100\n", + "664/664 [==============================] - 0s 228us/step - loss: 0.3658 - acc: 0.8479\n", + "Epoch 78/100\n", + "664/664 [==============================] - 0s 224us/step - loss: 0.3652 - acc: 0.8464\n", + "Epoch 79/100\n", + "664/664 [==============================] - 0s 221us/step - loss: 0.3651 - acc: 0.8509\n", + "Epoch 80/100\n", + "664/664 [==============================] - 0s 218us/step - loss: 0.3652 - acc: 0.8524\n", + "Epoch 81/100\n", + "664/664 [==============================] - 0s 245us/step - loss: 0.3648 - acc: 0.8464\n", + "Epoch 82/100\n", + "664/664 [==============================] - 0s 243us/step - loss: 0.3648 - acc: 0.8539\n", + "Epoch 83/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "664/664 [==============================] - 0s 218us/step - loss: 0.3646 - acc: 0.8494\n", + "Epoch 84/100\n", + "664/664 [==============================] - 0s 228us/step - loss: 0.3644 - acc: 0.8494\n", + "Epoch 85/100\n", + "664/664 [==============================] - 0s 207us/step - loss: 0.3641 - acc: 0.8464\n", + "Epoch 86/100\n", + "664/664 [==============================] - 0s 206us/step - loss: 0.3642 - acc: 0.8524\n", + "Epoch 87/100\n", + "664/664 [==============================] - 0s 201us/step - loss: 0.3637 - acc: 0.8509\n", + "Epoch 88/100\n", + "664/664 [==============================] - 0s 228us/step - loss: 0.3637 - acc: 0.8524\n", + "Epoch 89/100\n", + "664/664 [==============================] - 0s 218us/step - loss: 0.3639 - acc: 0.8554\n", + "Epoch 90/100\n", + "664/664 [==============================] - 0s 227us/step - loss: 0.3637 - acc: 0.8539\n", + "Epoch 91/100\n", + "664/664 [==============================] - 0s 212us/step - loss: 0.3636 - acc: 0.8449\n", + "Epoch 92/100\n", + "664/664 [==============================] - 0s 201us/step - loss: 0.3639 - acc: 0.8524\n", + "Epoch 93/100\n", + "664/664 [==============================] - 0s 204us/step - loss: 0.3636 - acc: 0.8464\n", + "Epoch 94/100\n", + "664/664 [==============================] - 0s 206us/step - loss: 0.3637 - acc: 0.8479\n", + "Epoch 95/100\n", + "664/664 [==============================] - 0s 201us/step - loss: 0.3629 - acc: 0.8494\n", + "Epoch 96/100\n", + "664/664 [==============================] - 0s 200us/step - loss: 0.3631 - acc: 0.8479\n", + "Epoch 97/100\n", + "664/664 [==============================] - 0s 204us/step - loss: 0.3634 - acc: 0.8524\n", + "Epoch 98/100\n", + "664/664 [==============================] - 0s 203us/step - loss: 0.3633 - acc: 0.8524\n", + "Epoch 99/100\n", + "664/664 [==============================] - 0s 204us/step - loss: 0.3629 - acc: 0.8509\n", + "Epoch 100/100\n", + "664/664 [==============================] - 0s 204us/step - loss: 0.3631 - acc: 0.8494\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier.fit(X_train, y_train, batch_size=5, nb_epoch=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = classifier.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = (y_pred > 0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "cm = confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[75, 15],\n", + " [13, 63]], dtype=int64)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cm" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8132530120481928" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(63+72) / (63+16+15+72)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.76 0.82 0.79 79\n", + " 1 0.83 0.77 0.80 87\n", + "\n", + " micro avg 0.80 0.80 0.80 166\n", + " macro avg 0.80 0.80 0.80 166\n", + "weighted avg 0.80 0.80 0.80 166\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 0.71 0.80 0.75 79\n", + " 1 0.79 0.70 0.74 87\n", + "\n", + " micro avg 0.75 0.75 0.75 166\n", + " macro avg 0.75 0.75 0.75 166\n", + "weighted avg 0.75 0.75 0.75 166\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 0.76 0.84 0.80 79\n", + " 1 0.84 0.76 0.80 87\n", + "\n", + " micro avg 0.80 0.80 0.80 166\n", + " macro avg 0.80 0.80 0.80 166\n", + "weighted avg 0.80 0.80 0.80 166\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 0.75 0.82 0.78 79\n", + " 1 0.82 0.75 0.78 87\n", + "\n", + " micro avg 0.78 0.78 0.78 166\n", + " macro avg 0.78 0.78 0.78 166\n", + "weighted avg 0.79 0.78 0.78 166\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 0.52 0.58 0.55 79\n", + " 1 0.58 0.52 0.55 87\n", + "\n", + " micro avg 0.55 0.55 0.55 166\n", + " macro avg 0.55 0.55 0.55 166\n", + "weighted avg 0.55 0.55 0.55 166\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report\n", + "print(classification_report(Y_TEST,pred2))\n", + "print(classification_report(Y_TEST,pred3))\n", + "print(classification_report(Y_TEST,pred4))\n", + "print(classification_report(Y_TEST,pred_6))\n", + "print(classification_report(Y_TEST,y_pred))" + ] + } + ], + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/parthpathak1998@gmail.com/Project/Report AITS.docx b/parthpathak1998@gmail.com/Project/Report AITS.docx new file mode 100644 index 000000000..fed19803e Binary files /dev/null and b/parthpathak1998@gmail.com/Project/Report AITS.docx differ