From dd4d75366486c4144a9e99e4f60c6d9e7c54323f Mon Sep 17 00:00:00 2001 From: ishant06 <98818077+ishant06@users.noreply.github.com> Date: Wed, 16 Nov 2022 04:23:38 +0530 Subject: [PATCH] Add files via upload --- 1.fibonacci_iterative.cpp | 23 + 1.fibonacci_recursive.cpp | 20 + 2.huffmanCoding.cpp | 66 + 3.fractionalKnapsack.cpp | 42 + 4.0_1Knapsack.cpp | 54 + 5.nQueenProblem.cpp | 56 + ML Assignment 1.ipynb | 720 + ML Assignment 2.ipynb | 591 + ML Assignment 4.ipynb | 149 + ML Assignment 5.ipynb | 432 + ML Assignment 6.ipynb | 27652 ++++++++++++++++++++++++++++++++++++ 11 files changed, 29805 insertions(+) create mode 100644 1.fibonacci_iterative.cpp create mode 100644 1.fibonacci_recursive.cpp create mode 100644 2.huffmanCoding.cpp create mode 100644 3.fractionalKnapsack.cpp create mode 100644 4.0_1Knapsack.cpp create mode 100644 5.nQueenProblem.cpp create mode 100644 ML Assignment 1.ipynb create mode 100644 ML Assignment 2.ipynb create mode 100644 ML Assignment 4.ipynb create mode 100644 ML Assignment 5.ipynb create mode 100644 ML Assignment 6.ipynb diff --git a/1.fibonacci_iterative.cpp b/1.fibonacci_iterative.cpp new file mode 100644 index 0000000..953016e --- /dev/null +++ b/1.fibonacci_iterative.cpp @@ -0,0 +1,23 @@ +#include +using namespace std; +typedef long long ll; +void printFibonacciNumbers(int n){ + if(n>=0)cout<<0<<" "; + if(n>=1)cout<<1<<" "; + int prev2=0,prev1=1; + for(int i=2;i>n; + printFibonacciNumbers(n); + return 0; + +} diff --git a/1.fibonacci_recursive.cpp b/1.fibonacci_recursive.cpp new file mode 100644 index 0000000..226afaf --- /dev/null +++ b/1.fibonacci_recursive.cpp @@ -0,0 +1,20 @@ +#include +using namespace std; +typedef long long ll; +int func(int n,vector&dp){ + if(dp[n]!=-1)return dp[n]; + if(n==0 || n==1)return n; + int num=func(n-1,dp)+func(n-2,dp); + return dp[n]=num; +} +int main(){ + int n; + cout<<"Enter Number"<>n; + vectordp(n+1,-1); + for(int i=0;i +using namespace std; +typedef long long ll; + +class Node{ + public: + char data; + int freq; + Node *left,*right; + Node(char data,int freq){ + this->data=data; + this->freq=freq; + left=right=NULL; + } +}; + +class cmp{ + public: + bool operator()(Node* a,Node* b){ + return a->freq>b->freq; + } +}; + +void printTree(Node *head,string str=""){ + if(head==NULL)return; + if(head->left==NULL && head->right==NULL){ + cout<data<<" -> "<left,str+"0"); + printTree(head->right,str+"1"); +} + +Node* buildTree(vector&arr,vector&frequency){ + priority_queue,cmp>pq; + for(int i=0;i1){ + Node *a=pq.top(); + pq.pop(); + Node *b=pq.top(); + pq.pop(); + Node *c=new Node('$',a->freq+b->freq); + c->left=a; + c->right=b; + pq.push(c); + } + return pq.top(); +} + +int main(){ + int n; + cin>>n; + vectorarr(n); + vectorfrequency(n); + for(int i=0;i>arr[i]; + } + for(int i=0;i>frequency[i]; + } + Node *head=buildTree(arr,frequency); + printTree(head); + return 0; +} diff --git a/3.fractionalKnapsack.cpp b/3.fractionalKnapsack.cpp new file mode 100644 index 0000000..f23461e --- /dev/null +++ b/3.fractionalKnapsack.cpp @@ -0,0 +1,42 @@ +#include +using namespace std; +typedef long long ll; +class Item{ + public: + int value; + int weight; +}; + +bool cmp(Item a,Item b){ + double r1=(double)a.value/(double)a.weight; + double r2=(double)b.value/(double)b.weight; + return r1>r2; +} + +double fractionalKnapsack(vectoritems,int W){ + sort(items.begin(),items.end(),cmp); + double mx=0; + for(int i=0;i>n>>W; + vectoritems(n); + for(int i=0;i>items[i].value; + cin>>items[i].weight; + } + double maxValue=fractionalKnapsack(items,W); + cout< +using namespace std; +typedef long long ll; +int func(int i,int j,vector &val,vector &wt,vector> &dp){ + if(i==0){ + if(wt[i]<=j)return val[i]; + else return 0; + } + if(dp[i][j]!=-1)return dp[i][j]; + int notTake=func(i-1,j,val,wt,dp); + int take=0; + if(wt[i]<=j){ + take=val[i]+func(i-1,j-wt[i],val,wt,dp); + } + return dp[i][j]=max(take,notTake); +} +int knapsack(int n,vector&val,vector&wt,int W){ + vector>dp(n,vector(W+1,-1)); + int ans=func(n-1,W,val,wt,dp); + + return ans; +} + +// int knapsack(int n,vector&val,vector&wt,int W){ +// iterative approach +// can be space optimized +// vector>dp(n,vector(W+1,0)); +// for(int j=wt[0];j<=W;j++)dp[0][j]=val[0]; +// for(int i=1;i>n>>W; + vectorval(n),wt(n); + for(int i=0;i>val[i]; + } + for(int i=0;i>wt[i]; + } + int maxValue=knapsack(n,val,wt,W); + cout< +using namespace std; +typedef long long ll; +bool isValid(int row,int col,vector>&board,int n){ + + int dup_row=row; + int dup_col=col; + while(col>=0 && row>=0){ + if(board[row][col]=='Q')return false; + row--; + col--; + } + row=dup_row; + col=dup_col; + while(col>=0){ + if(board[row][col]=='Q')return false; + col--; + } + col=dup_col; + while(row=0){ + if(board[row][col]=='Q')return false; + row++; + col--; + } + return true; +} +bool placeQueen(int col,vector>&board,int n){ + if(col==n)return true; + for(int i=0;i>n; + vector>board(n,vector(n)); + for(int i=0;i 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] + } + ], + "source": [ + "#Importing required libraries\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6679414b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " fare_amount pickup_datetime pickup_longitude pickup_latitude \\\n", + "0 7.5 2015-05-07 19:52:06 UTC -73.999817 40.738354 \n", + "1 7.7 2009-07-17 20:04:56 UTC -73.994355 40.728225 \n", + "2 12.9 2009-08-24 21:45:00 UTC -74.005043 40.740770 \n", + "3 5.3 2009-06-26 08:22:21 UTC -73.976124 40.790844 \n", + "4 16.0 2014-08-28 17:47:00 UTC -73.925023 40.744085 \n", + "\n", + " dropoff_longitude dropoff_latitude passenger_count Distance \n", + "0 -73.999512 40.723217 1 1.68 \n", + "1 -73.994710 40.750325 1 2.46 \n", + "2 -73.962565 40.772647 1 5.04 \n", + "3 -73.965316 40.803349 3 1.66 \n", + "4 -73.973082 40.761247 5 4.48 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def haversine (lon_1, lon_2, lat_1, lat_2): #Function to find the distance using the coordinates\n", + " lon_1, lon_2, lat_1, lat_2 = map(np.radians, [lon_1, lon_2, lat_1, lat_2]) #Converting Degrees to Radians\n", + " diff_lon = lon_2 - lon_1\n", + " diff_lat = lat_2 - lat_1\n", + " distance = 2 * 6371 * np.arcsin(np.sqrt(np.sin(diff_lat/2.0)**2+np.cos(lat_1)*np.cos(lat_2)*np.sin(diff_lon/2.0)**2)) #Calculationg the Distance using Haversine Formula\n", + " return distance\n", + "\n", + "df['Distance']= haversine(df['pickup_longitude'],df['dropoff_longitude'],df['pickup_latitude'],df['dropoff_latitude'])\n", + "df['Distance'] = df['Distance'].astype(float).round(2) #Rounding-off to 2 decimals\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36786888", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'fare_amount')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Plotting a scatter plot to check for outliers.\n", + "plt.scatter(df['Distance'], df['fare_amount'])\n", + "plt.xlabel(\"Distance\")\n", + "plt.ylabel(\"fare_amount\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f67e997e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'fare_amount')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Dealing with Outliers via removing rows with too large Distance and 0 or lesser distance.\n", + "df.drop(df[df['Distance']>60].index, inplace=True)\n", + "df.drop(df[df['Distance']==0].index, inplace=True)\n", + "df.drop(df[df['Distance']<0].index, inplace=True)\n", + "#Dealing with Outliers via removing rows with 0 or lesser fare amounts.\n", + "df.drop(df[df['fare_amount']==0].index, inplace=True)\n", + "df.drop(df[df['fare_amount']<0].index, inplace=True)\n", + "#Dealing with Outliers via removing rows with non-plausible fare amounts and distance travelled.\n", + "df.drop(df[df['Distance']>100].index, inplace=True)\n", + "df.drop(df[df['fare_amount']>100].index, inplace=True)\n", + "df.drop(df[(df['fare_amount']>100) & (df['Distance']<1)].index, inplace = True )\n", + "df.drop(df[(df['fare_amount']<100) & (df['Distance']>100)].index, inplace = True )\n", + "#Plotting a Scatter Plot to check for any more outliers and also to show correlation between Fare Amount and Distance.\n", + "plt.scatter(df['Distance'], df['fare_amount'])\n", + "plt.xlabel(\"Distance\")\n", + "plt.ylabel(\"fare_amount\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1cf60faf", + "metadata": {}, + "outputs": [], + "source": [ + "#Preprocessing the Data Using Standard Scaler in range of -1 to 1\n", + "x = df['Distance'].values.reshape(-1, 1) #Independent Variable\n", + "y = df['fare_amount'].values.reshape(-1, 1) #Dependent Variable\n", + "std = StandardScaler()\n", + "Y = std.fit_transform(y)\n", + "X = std.fit_transform(x)\n", + "#Splitting the data into training and testing set\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "882b0547", + "metadata": {}, + "outputs": [], + "source": [ + "def apply_model(model): #Model to print the metrics of the various prediction models\n", + " model.fit(X_train,Y_train)\n", + " print(\"Training score = \",model.score(X_train,Y_train))\n", + " print(\"Testing score = \",model.score(X_test,Y_test))\n", + " print(\"Accuracy = \",model.score(X_test,Y_test))\n", + " Y_pred = model.predict(X_test)\n", + " print(\"Predicted values:\\n\",Y_pred)\n", + " print(\"Mean Absolute Error =\", metrics.mean_absolute_error(Y_test, Y_pred))\n", + " print(\"Mean Squared Error =\", metrics.mean_squared_error(Y_test, Y_pred))\n", + " print(\"Root Mean Squared Error =\", np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c99e0db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training score = 0.8023890708990102\n", + "Testing score = 0.800134921063358\n", + "Accuracy = 0.800134921063358\n", + "Predicted values:\n", + " [[-0.0856421 ]\n", + " [ 1.40250073]\n", + " [ 0.1072653 ]\n", + " ...\n", + " [-0.17833787]\n", + " [-0.42636167]\n", + " [-0.37124527]]\n", + "Mean Absolute Error = 0.243543639885431\n", + "Mean Squared Error = 0.19732734085539588\n", + "Root Mean Squared Error = 0.44421542167668593\n" + ] + } + ], + "source": [ + "lr = LinearRegression()\n", + "apply_model(lr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1cf9422a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\candr\\AppData\\Local\\Temp\\ipykernel_7216\\3813684645.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", + " model.fit(X_train,Y_train)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training score = 0.8250567049453948\n", + "Testing score = 0.7931312012692804\n", + "Accuracy = 0.7931312012692804\n", + "Predicted values:\n", + " [-0.10304075 1.80284551 0.08764113 ... -0.21391608 -0.42011423\n", + " -0.37785255]\n", + "Mean Absolute Error = 0.24703500001737674\n", + "Mean Squared Error = 0.20424213262599705\n", + "Root Mean Squared Error = 0.4519315574575392\n" + ] + } + ], + "source": [ + "#Random Forest Model\n", + "rf = RandomForestRegressor(n_estimators=100, random_state=10)\n", + "apply_model(rf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef4d3f3f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.8 64-bit (microsoft store)", + "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": "3.10.8" + }, + "vscode": { + "interpreter": { + "hash": "5232988f57ad2f072fe47567137ff76972545fba8cee65a4c3ca12237f606f5e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ML Assignment 2.ipynb b/ML Assignment 2.ipynb new file mode 100644 index 0000000..db3bc38 --- /dev/null +++ b/ML Assignment 2.ipynb @@ -0,0 +1,591 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c6a69e17", + "metadata": {}, + "source": [ + "# Classify the email using the binary classification method. \n", + "## Email Spam detection has two states: \n", + " a) Normal State – Not Spam\n", + " b) Abnormal State – Spam. \n", + "Use K-Nearest Neighbors and Support Vector Machine for classification. Analyze their performance.\n", + "\n", + "Dataset link: The emails.csv dataset on the Kaggle\n", + "https://www.kaggle.com/datasets/balaka18/email-spam-classification-dataset-csv" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1f622335", + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'sklearn'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn [1], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmodel_selection\u001b[39;00m \u001b[39mimport\u001b[39;00m train_test_split\n\u001b[0;32m 4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mneighbors\u001b[39;00m \u001b[39mimport\u001b[39;00m KNeighborsClassifier\n\u001b[0;32m 5\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39msvm\u001b[39;00m \u001b[39mimport\u001b[39;00m SVC\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import confusion_matrix, classification_report,accuracy_score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db221406", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Email No.thetoectandforofayouhou...conneveyjayvaluedlayinfrastructuremilitaryallowingffdryPrediction
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" + ], + "text/plain": [ + " Email No. the to ect and for of a you hou ... connevey jay \\\n", + "0 Email 1 0 0 1 0 0 0 2 0 0 ... 0 0 \n", + "1 Email 2 8 13 24 6 6 2 102 1 27 ... 0 0 \n", + "2 Email 3 0 0 1 0 0 0 8 0 0 ... 0 0 \n", + "3 Email 4 0 5 22 0 5 1 51 2 10 ... 0 0 \n", + "4 Email 5 7 6 17 1 5 2 57 0 9 ... 0 0 \n", + "\n", + " valued lay infrastructure military allowing ff dry Prediction \n", + "0 0 0 0 0 0 0 0 0 \n", + "1 0 0 0 0 0 1 0 0 \n", + "2 0 0 0 0 0 0 0 0 \n", + "3 0 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 1 0 0 \n", + "\n", + "[5 rows x 3002 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df=pd.read_csv(\"emails.csv\") #Reading the Dataset\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1ad4120", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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thetoectandforofayouhouin...conneveyjayvaluedlayinfrastructuremilitaryallowingffdryPrediction
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" + ], + "text/plain": [ + " the to ect and for of a you hou in ... connevey jay valued \\\n", + "0 0 0 1 0 0 0 2 0 0 0 ... 0 0 0 \n", + "1 8 13 24 6 6 2 102 1 27 18 ... 0 0 0 \n", + "2 0 0 1 0 0 0 8 0 0 4 ... 0 0 0 \n", + "3 0 5 22 0 5 1 51 2 10 1 ... 0 0 0 \n", + "4 7 6 17 1 5 2 57 0 9 3 ... 0 0 0 \n", + "\n", + " lay infrastructure military allowing ff dry Prediction \n", + "0 0 0 0 0 0 0 0 \n", + "1 0 0 0 0 1 0 0 \n", + "2 0 0 0 0 0 0 0 \n", + "3 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 1 0 0 \n", + "\n", + "[5 rows x 3001 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(columns=['Email No.'], inplace=True) #Dropping Email No. as it is irrelevant.\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0b24581", + "metadata": {}, + "outputs": [], + "source": [ + "#Splitting the Dataset\n", + "X=df.iloc[:, :df.shape[1]-1]\n", + "Y=df.iloc[:, -1]\n", + "X_train, X_test, Y_train, Y_test=train_test_split(X, Y, test_size=0.20, random_state=8) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2a38bda", + "metadata": {}, + "outputs": [], + "source": [ + "def apply_model(model):#Model to print the scores of various models\n", + " model.fit(X_train,Y_train)\n", + " print(\"Training score = \",model.score(X_train,Y_train))\n", + " print(\"Testing score = \",model.score(X_test,Y_test))\n", + " print(\"Accuracy = \",model.score(X_test,Y_test))\n", + " Y_pred = model.predict(X_test)\n", + " print(\"Predicted values:\\n\",Y_pred)\n", + " print(\"Confusion Matrix:\\n\",confusion_matrix(Y_test,Y_pred))\n", + " print(\"Classification Report:\\n\",classification_report(Y_test,Y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b9cdd23", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training score = 0.883248730964467\n", + "Testing score = 0.8695652173913043\n", + "Accuracy = 0.8695652173913043\n", + "Predicted values:\n", + " [0 0 0 ... 0 0 0]\n", + "Confusion Matrix:\n", + " [[653 73]\n", + " [ 62 247]]\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.91 0.90 0.91 726\n", + " 1 0.77 0.80 0.79 309\n", + "\n", + " accuracy 0.87 1035\n", + " macro avg 0.84 0.85 0.85 1035\n", + "weighted avg 0.87 0.87 0.87 1035\n", + "\n" + ] + } + ], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=17) #KNN Model\n", + "apply_model(knn)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3712b36", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\candr\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning: Solver terminated early (max_iter=10000). Consider pre-processing your data with StandardScaler or MinMaxScaler.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training score = 0.9951655789219241\n", + "Testing score = 0.9671497584541063\n", + "Accuracy = 0.9671497584541063\n", + "Predicted values:\n", + " [0 1 0 ... 1 0 0]\n", + "Confusion Matrix:\n", + " [[710 16]\n", + " [ 18 291]]\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.98 0.98 0.98 726\n", + " 1 0.95 0.94 0.94 309\n", + "\n", + " accuracy 0.97 1035\n", + " macro avg 0.96 0.96 0.96 1035\n", + "weighted avg 0.97 0.97 0.97 1035\n", + "\n" + ] + } + ], + "source": [ + "svm = SVC(kernel='linear',random_state=3,max_iter=10000) #SVM Model\n", + "apply_model(svm)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.8 64-bit (microsoft store)", + "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": "3.10.8" + }, + "vscode": { + "interpreter": { + "hash": "5232988f57ad2f072fe47567137ff76972545fba8cee65a4c3ca12237f606f5e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ML Assignment 4.ipynb b/ML Assignment 4.ipynb new file mode 100644 index 0000000..adc5ae9 --- /dev/null +++ b/ML Assignment 4.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a5ffc597", + "metadata": {}, + "source": [ + "# LP3 Group B Assignment 4\n", + "## Implement Gradient Descent Algorithm to find the local minima of a function. \n", + "### For example, find the local minima of the function y=(x+3)² starting from the point x=2 ." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2ce43769", + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn [6], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmean_squared_error\u001b[39m(y_true, y_predicted):\n\u001b[0;32m 5\u001b[0m cost \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msum((y_true\u001b[39m-\u001b[39my_predicted)\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m) \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(y_true) \u001b[39m#Calculating the loss or cost\u001b[39;00m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + " \n", + "def mean_squared_error(y_true, y_predicted):\n", + " cost = np.sum((y_true-y_predicted)**2) / len(y_true) #Calculating the loss or cost\n", + " return cost\n", + " \n", + "def gradient_descent(x, y, iterations = 1000, learning_rate = 0.0001, stopping_threshold = 1e-6): #Gradient Descent Function\n", + " #Initializing weight, bias, learning rate and iterations\n", + " current_weight = 0.1\n", + " current_bias = 0.01\n", + " iterations = iterations\n", + " learning_rate = learning_rate\n", + " n = float(len(x))\n", + " costs = []\n", + " weights = []\n", + " previous_cost = None\n", + " \n", + " for i in range(iterations): #Estimation of optimal parameters\n", + " y_predicted = (current_weight * x) + current_bias #Making predictions\n", + " current_cost = mean_squared_error(y, y_predicted) #Calculationg the current cost\n", + " #If the change in cost is less than or equal to stopping_threshold we stop the gradient descent\n", + " if previous_cost and abs(previous_cost-current_cost)<=stopping_threshold:\n", + " break\n", + " previous_cost = current_cost\n", + " costs.append(current_cost)\n", + " weights.append(current_weight)\n", + " #Calculating the gradients\n", + " weight_derivative = -(2/n) * sum(x * (y-y_predicted)) \n", + " bias_derivative = -(2/n) * sum(y-y_predicted)\n", + " #Updating weights and bias\n", + " current_weight = current_weight - (learning_rate * weight_derivative)\n", + " current_bias = current_bias - (learning_rate * bias_derivative) \n", + " #Printing the parameters for each 1000th iteration\n", + " print(f\"Iteration{i+1}:Cost{current_cost},Weight\\{current_weight},Bias{current_bias}\")\n", + " #Visualizing the weights and cost at for all iterations\n", + " plt.figure(figsize = (8,6))\n", + " plt.plot(weights, costs)\n", + " plt.scatter(weights, costs, marker='o', color='red')\n", + " plt.title(\"Cost vs Weights\")\n", + " plt.ylabel(\"Cost\")\n", + " plt.xlabel(\"Weight\")\n", + " plt.show()\n", + " return current_weight, current_bias\n", + "\n", + "#Data\n", + "X = np.array([32.50234527, 53.42680403, 61.53035803, 47.47563963, 59.81320787,\n", + " 55.14218841, 52.21179669, 39.29956669, 48.10504169, 52.55001444,\n", + " 45.41973014, 54.35163488, 44.1640495 , 58.16847072, 56.72720806,\n", + " 48.95588857, 44.68719623, 60.29732685, 45.61864377, 38.81681754])\n", + "Y = np.array([31.70700585, 68.77759598, 62.5623823 , 71.54663223, 87.23092513,\n", + " 78.21151827, 79.64197305, 59.17148932, 75.3312423 , 71.30087989,\n", + " 55.16567715, 82.47884676, 62.00892325, 75.39287043, 81.43619216,\n", + " 60.72360244, 82.89250373, 97.37989686, 48.84715332, 56.87721319])\n", + " \n", + "#Estimating weight and bias using gradient descent\n", + "estimated_weight, eatimated_bias = gradient_descent(X, Y, iterations=2000)\n", + "print(f\"Estimated Weight: {estimated_weight}\\nEstimated Bias: {eatimated_bias}\")\n", + "Y_pred = estimated_weight*X + eatimated_bias #Making predictions using estimated parameters\n", + "#Plotting the regression line\n", + "plt.figure(figsize = (8,6))\n", + "plt.scatter(X, Y, marker='o', color='red')\n", + "plt.plot([min(X), max(X)], [min(Y_pred), max(Y_pred)], color='blue',markerfacecolor='red',markersize=10,linestyle='dashed')\n", + "plt.xlabel(\"X\")\n", + "plt.ylabel(\"Y\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ee083f1e", + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn [7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] + } + ], + "source": [ + "import matplotlib " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.8 64-bit (microsoft store)", + "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": "3.10.8" + }, + "vscode": { + "interpreter": { + "hash": "5232988f57ad2f072fe47567137ff76972545fba8cee65a4c3ca12237f606f5e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ML Assignment 5.ipynb b/ML Assignment 5.ipynb new file mode 100644 index 0000000..d73c73d --- /dev/null +++ b/ML Assignment 5.ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dc994e84", + "metadata": {}, + "source": [ + "# LP3 Group B Assignment 5\n", + "## Implement K-Nearest Neighbors algorithm on diabetes.csv dataset. Compute confusion matrix, accuracy, error rate, precision and recall on the given dataset.\n", + "Dataset link : https://www.kaggle.com/datasets/abdallamahgoub/diabetes" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bb13b804", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b3ec6fa1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", + "0 6 148 72 35 0 33.6 \n", + "1 1 85 66 29 0 26.6 \n", + "2 8 183 64 0 0 23.3 \n", + "3 1 89 66 23 94 28.1 \n", + "4 0 137 40 35 168 43.1 \n", + "\n", + " Pedigree Age Outcome \n", + "0 0.627 50 1 \n", + "1 0.351 31 0 \n", + "2 0.672 32 1 \n", + "3 0.167 21 0 \n", + "4 2.288 33 1 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df=pd.read_csv(\"diabetes.csv\") #Reading the Dataset\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7fb599b7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pregnancies int64\n", + "Glucose int64\n", + "BloodPressure int64\n", + "SkinThickness int64\n", + "Insulin int64\n", + "BMI float64\n", + "Pedigree float64\n", + "Age int64\n", + "Outcome int64\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "14fbeb12", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", + "0 6 148.0 72.0 35.000000 79.799479 33.6 \n", + "1 1 85.0 66.0 29.000000 79.799479 26.6 \n", + "2 8 183.0 64.0 20.536458 79.799479 23.3 \n", + "3 1 89.0 66.0 23.000000 94.000000 28.1 \n", + "4 0 137.0 40.0 35.000000 168.000000 43.1 \n", + "\n", + " Pedigree Age Outcome \n", + "0 0.627 50 1 \n", + "1 0.351 31 0 \n", + "2 0.672 32 1 \n", + "3 0.167 21 0 \n", + "4 2.288 33 1 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Glucose\"].replace(0,df[\"Glucose\"].mean(), inplace=True)\n", + "df[\"BloodPressure\"].replace(0,df[\"BloodPressure\"].mean(), inplace=True)\n", + "df[\"SkinThickness\"].replace(0,df[\"SkinThickness\"].mean(), inplace=True)\n", + "df[\"Insulin\"].replace(0,df[\"Insulin\"].mean(), inplace=True)\n", + "df[\"BMI\"].replace(0,df[\"BMI\"].mean(), inplace=True)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f8db7688", + "metadata": {}, + "outputs": [], + "source": [ + "X = df.iloc[:, :8]\n", + "Y = df.iloc[:, 8:]\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.20,random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "089e6478", + "metadata": {}, + "outputs": [], + "source": [ + "def apply_model(model):#Model to print the scores of various models\n", + " model.fit(X_train,Y_train)\n", + " print(\"Training score = \",model.score(X_train,Y_train))\n", + " print(\"Testing score = \",model.score(X_test,Y_test))\n", + " print(\"Accuracy = \",model.score(X_test,Y_test))\n", + " Y_pred = model.predict(X_test)\n", + " print(\"Predicted values:\\n\",Y_pred)\n", + " print(\"Confusion Matrix:\\n\",confusion_matrix(Y_test,Y_pred))\n", + " print(\"Classification Report:\\n\",classification_report(Y_test,Y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "442015b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training score = 0.7915309446254072\n", + "Testing score = 0.7597402597402597\n", + "Accuracy = 0.7597402597402597\n", + "Predicted values:\n", + " [1 0 0 1 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1\n", + " 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1 1 0 0 0 0 1\n", + " 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0\n", + " 0 1 0 1 1 0 1 0 1 1 0 0 0 0 0 0 1 1 1 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0\n", + " 0 0 0 0 0 0]\n", + "Confusion Matrix:\n", + " [[89 18]\n", + " [19 28]]\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.83 0.83 107\n", + " 1 0.61 0.60 0.60 47\n", + "\n", + " accuracy 0.76 154\n", + " macro avg 0.72 0.71 0.72 154\n", + "weighted avg 0.76 0.76 0.76 154\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\candr\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", + " return self._fit(X, y)\n" + ] + } + ], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=5) #KNN Model\n", + "apply_model(knn)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f63cd23", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.8 ('3': venv)", + "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": "3.10.8" + }, + "vscode": { + "interpreter": { + "hash": "30169576f97bbc511108375109808e2d217fd6a88f0e16324d22f57023d063d9" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ML Assignment 6.ipynb b/ML Assignment 6.ipynb new file mode 100644 index 0000000..cc68747 --- /dev/null +++ b/ML Assignment 6.ipynb @@ -0,0 +1,27652 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dd2b067e", + "metadata": {}, + "source": [ + "# LP3 Group B Assignment 6\n", + "## Implement K-Means clustering/ hierarchical clustering on sales_data_sample.csv dataset. Determine the number of clusters using the elbow method.\n", + "Dataset link : https://www.kaggle.com/datasets/kyanyoga/sample-sales-data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4bbc4254", + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'pandas'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn [2], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39m#Importing the required libraries\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pandas'" + ] + } + ], + "source": [ + "#Importing the required libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import plotly.express as px\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.cluster import KMeans" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "becfecbb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ORDERNUMBERQUANTITYORDEREDPRICEEACHORDERLINENUMBERSALESORDERDATESTATUSQTR_IDMONTH_IDYEAR_ID...ADDRESSLINE1ADDRESSLINE2CITYSTATEPOSTALCODECOUNTRYTERRITORYCONTACTLASTNAMECONTACTFIRSTNAMEDEALSIZE
0101073095.7022871.002/24/2003 0:00Shipped122003...897 Long Airport AvenueNaNNYCNY10022USANaNYuKwaiSmall
1101213481.3552765.905/7/2003 0:00Shipped252003...59 rue de l'AbbayeNaNReimsNaN51100FranceEMEAHenriotPaulSmall
2101344194.7423884.347/1/2003 0:00Shipped372003...27 rue du Colonel Pierre AviaNaNParisNaN75508FranceEMEADa CunhaDanielMedium
3101454583.2663746.708/25/2003 0:00Shipped382003...78934 Hillside Dr.NaNPasadenaCA90003USANaNYoungJulieMedium
41015949100.00145205.2710/10/2003 0:00Shipped4102003...7734 Strong St.NaNSan FranciscoCANaNUSANaNBrownJulieMedium
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5 rows × 25 columns

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" + ], + "text/plain": [ + " ORDERNUMBER QUANTITYORDERED PRICEEACH ORDERLINENUMBER SALES \\\n", + "0 10107 30 95.70 2 2871.00 \n", + "1 10121 34 81.35 5 2765.90 \n", + "2 10134 41 94.74 2 3884.34 \n", + "3 10145 45 83.26 6 3746.70 \n", + "4 10159 49 100.00 14 5205.27 \n", + "\n", + " ORDERDATE STATUS QTR_ID MONTH_ID YEAR_ID ... \\\n", + "0 2/24/2003 0:00 Shipped 1 2 2003 ... \n", + "1 5/7/2003 0:00 Shipped 2 5 2003 ... \n", + "2 7/1/2003 0:00 Shipped 3 7 2003 ... \n", + "3 8/25/2003 0:00 Shipped 3 8 2003 ... \n", + "4 10/10/2003 0:00 Shipped 4 10 2003 ... \n", + "\n", + " ADDRESSLINE1 ADDRESSLINE2 CITY STATE \\\n", + "0 897 Long Airport Avenue NaN NYC NY \n", + "1 59 rue de l'Abbaye NaN Reims NaN \n", + "2 27 rue du Colonel Pierre Avia NaN Paris NaN \n", + "3 78934 Hillside Dr. NaN Pasadena CA \n", + "4 7734 Strong St. NaN San Francisco CA \n", + "\n", + " POSTALCODE COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE \n", + "0 10022 USA NaN Yu Kwai Small \n", + "1 51100 France EMEA Henriot Paul Small \n", + "2 75508 France EMEA Da Cunha Daniel Medium \n", + "3 90003 USA NaN Young Julie Medium \n", + "4 NaN USA NaN Brown Julie Medium \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('sales_data_sample.csv', encoding = 'unicode_escape') #Reading the csv file.\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc3688d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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QUANTITYORDEREDPRICEEACHORDERLINENUMBERSALESSTATUSMONTH_IDYEAR_IDPRODUCTLINEMSRPPRODUCTCODECOUNTRYDEALSIZE
03095.7022871.00Shipped22003Motorcycles95S10_1678USASmall
13481.3552765.90Shipped52003Motorcycles95S10_1678FranceSmall
24194.7423884.34Shipped72003Motorcycles95S10_1678FranceMedium
34583.2663746.70Shipped82003Motorcycles95S10_1678USAMedium
449100.00145205.27Shipped102003Motorcycles95S10_1678USAMedium
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" + ], + "text/plain": [ + " QUANTITYORDERED PRICEEACH ORDERLINENUMBER SALES STATUS MONTH_ID \\\n", + "0 30 95.70 2 2871.00 Shipped 2 \n", + "1 34 81.35 5 2765.90 Shipped 5 \n", + "2 41 94.74 2 3884.34 Shipped 7 \n", + "3 45 83.26 6 3746.70 Shipped 8 \n", + "4 49 100.00 14 5205.27 Shipped 10 \n", + "\n", + " YEAR_ID PRODUCTLINE MSRP PRODUCTCODE COUNTRY DEALSIZE \n", + "0 2003 Motorcycles 95 S10_1678 USA Small \n", + "1 2003 Motorcycles 95 S10_1678 France Small \n", + "2 2003 Motorcycles 95 S10_1678 France Medium \n", + "3 2003 Motorcycles 95 S10_1678 USA Medium \n", + "4 2003 Motorcycles 95 S10_1678 USA Medium " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Removing the coloumns which dont add value for the analysis.\n", + "to_drop = ['PHONE','ADDRESSLINE1','ADDRESSLINE2','CITY','STATE','POSTALCODE','TERRITORY','CONTACTLASTNAME','CONTACTFIRSTNAME','CUSTOMERNAME','ORDERNUMBER','QTR_ID','ORDERDATE']\n", + "df = df.drop(to_drop, axis=1)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "740ccf57", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "QUANTITYORDERED 58\n", + "PRICEEACH 1016\n", + "ORDERLINENUMBER 18\n", + "SALES 2763\n", + "STATUS 6\n", + "MONTH_ID 12\n", + "YEAR_ID 3\n", + "PRODUCTLINE 7\n", + "MSRP 80\n", + "PRODUCTCODE 109\n", + "COUNTRY 19\n", + "DEALSIZE 3\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.nunique() #Checking unique values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c234d5e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "QUANTITYORDERED 0\n", + "PRICEEACH 0\n", + "ORDERLINENUMBER 0\n", + "SALES 0\n", + "STATUS 0\n", + "MONTH_ID 0\n", + "YEAR_ID 0\n", + "PRODUCTLINE 0\n", + "MSRP 0\n", + "PRODUCTCODE 0\n", + "COUNTRY 0\n", + "DEALSIZE 0\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3cda6e3b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "QUANTITYORDERED int64\n", + "PRICEEACH float64\n", + "ORDERLINENUMBER int64\n", + "SALES float64\n", + "STATUS int64\n", + "MONTH_ID int64\n", + "YEAR_ID int64\n", + "MSRP int64\n", + "PRODUCTCODE int8\n", + "PRODUCTLINE_Classic Cars uint8\n", + "PRODUCTLINE_Motorcycles uint8\n", + "PRODUCTLINE_Planes uint8\n", + "PRODUCTLINE_Ships uint8\n", + "PRODUCTLINE_Trains uint8\n", + "PRODUCTLINE_Trucks and Buses uint8\n", + "PRODUCTLINE_Vintage Cars uint8\n", + "DEALSIZE_Large uint8\n", + "DEALSIZE_Medium uint8\n", + "DEALSIZE_Small uint8\n", + "COUNTRY_Australia uint8\n", + "COUNTRY_Austria uint8\n", + "COUNTRY_Belgium uint8\n", + "COUNTRY_Canada uint8\n", + "COUNTRY_Denmark uint8\n", + "COUNTRY_Finland uint8\n", + "COUNTRY_France uint8\n", + "COUNTRY_Germany uint8\n", + "COUNTRY_Ireland uint8\n", + "COUNTRY_Italy uint8\n", + "COUNTRY_Japan uint8\n", + "COUNTRY_Norway uint8\n", + "COUNTRY_Philippines uint8\n", + "COUNTRY_Singapore uint8\n", + "COUNTRY_Spain uint8\n", + "COUNTRY_Sweden uint8\n", + "COUNTRY_Switzerland uint8\n", + "COUNTRY_UK uint8\n", + "COUNTRY_USA uint8\n", + "dtype: object" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Encodning Categorical Variables for easier processing.\n", + "status_dict = {'Shipped':1, 'Cancelled':2, 'On Hold':2, 'Disputed':2, 'In Process':0, 'Resolved':0}\n", + "df['STATUS'].replace(status_dict, inplace=True)\n", + "df['PRODUCTCODE'] = pd.Categorical(df['PRODUCTCODE']).codes\n", + "df = pd.get_dummies(data=df, columns=['PRODUCTLINE', 'DEALSIZE', 'COUNTRY'])\n", + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acee38b4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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QUANTITYORDEREDPRICEEACHORDERLINENUMBERSALESSTATUSMONTH_IDYEAR_IDMSRPPRODUCTCODEPRODUCTLINE_Classic Cars...COUNTRY_JapanCOUNTRY_NorwayCOUNTRY_PhilippinesCOUNTRY_SingaporeCOUNTRY_SpainCOUNTRY_SwedenCOUNTRY_SwitzerlandCOUNTRY_UKCOUNTRY_USACluster
03095.7022871.001220039500...0000000010
13481.3552765.901520039500...0000000000
24194.7423884.341720039500...0000000003
34583.2663746.701820039500...0000000013
449100.00145205.2711020039500...0000000013
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5 rows × 39 columns

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" + ], + "text/plain": [ + " QUANTITYORDERED PRICEEACH ORDERLINENUMBER SALES STATUS MONTH_ID \\\n", + "0 30 95.70 2 2871.00 1 2 \n", + "1 34 81.35 5 2765.90 1 5 \n", + "2 41 94.74 2 3884.34 1 7 \n", + "3 45 83.26 6 3746.70 1 8 \n", + "4 49 100.00 14 5205.27 1 10 \n", + "\n", + " YEAR_ID MSRP PRODUCTCODE PRODUCTLINE_Classic Cars ... COUNTRY_Japan \\\n", + "0 2003 95 0 0 ... 0 \n", + "1 2003 95 0 0 ... 0 \n", + "2 2003 95 0 0 ... 0 \n", + "3 2003 95 0 0 ... 0 \n", + "4 2003 95 0 0 ... 0 \n", + "\n", + " COUNTRY_Norway COUNTRY_Philippines COUNTRY_Singapore COUNTRY_Spain \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 0 0 0 0 \n", + "4 0 0 0 0 \n", + "\n", + " COUNTRY_Sweden COUNTRY_Switzerland COUNTRY_UK COUNTRY_USA Cluster \n", + "0 0 0 0 1 0 \n", + "1 0 0 0 0 0 \n", + "2 0 0 0 0 3 \n", + "3 0 0 0 1 3 \n", + "4 0 0 0 1 3 \n", + "\n", + "[5 rows x 39 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Applying k-means with 5 clusters as the elbow seems to form at 5 clusters\n", + "km = KMeans(n_clusters=5, random_state=1)\n", + "km.fit(sdf)\n", + "cluster_labels = km.labels_\n", + "df = df.assign(Cluster=cluster_labels)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41d1cb4b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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QUANTITYORDEREDPRICEEACHORDERLINENUMBERSALESSTATUSMONTH_IDYEAR_IDMSRPPRODUCTCODEPRODUCTLINE_Classic Cars...COUNTRY_ItalyCOUNTRY_JapanCOUNTRY_NorwayCOUNTRY_PhilippinesCOUNTRY_SingaporeCOUNTRY_SpainCOUNTRY_SwedenCOUNTRY_SwitzerlandCOUNTRY_UKCOUNTRY_USA
Cluster
030.58576667.9913876.5757302030.4278380.9990887.0757302003.81843177.13047459.3859490.298358...0.0000000.0246350.0346720.0100360.0328470.1204380.0182480.0082120.0520070.359489
132.77358581.4094348.0471702991.5932081.0000007.5660382003.74528392.45283060.3773580.198113...1.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
247.22293099.7995545.3694278293.7532481.0382176.7707012003.910828158.18471326.2420380.617834...0.0445860.0191080.0318470.0063690.0254780.1082800.0127390.0000000.0254780.407643
337.80258995.6673066.3195794442.8140861.0080917.1375402003.805016117.73786445.1359220.422330...0.0000000.0137540.0339810.0113270.0283170.1245950.0202270.0177990.0485440.385113
434.79386083.8017116.7543863055.8490791.0657896.9298252003.82017586.07894789.5043860.000000...0.0000000.0219300.0000000.0000000.0175440.1710530.0438600.0000000.1008770.307018
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5 rows × 38 columns

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" + ], + "text/plain": [ + " QUANTITYORDERED PRICEEACH ORDERLINENUMBER SALES STATUS \\\n", + "Cluster \n", + "0 30.585766 67.991387 6.575730 2030.427838 0.999088 \n", + "1 32.773585 81.409434 8.047170 2991.593208 1.000000 \n", + "2 47.222930 99.799554 5.369427 8293.753248 1.038217 \n", + "3 37.802589 95.667306 6.319579 4442.814086 1.008091 \n", + "4 34.793860 83.801711 6.754386 3055.849079 1.065789 \n", + "\n", + " MONTH_ID YEAR_ID MSRP PRODUCTCODE \\\n", + "Cluster \n", + "0 7.075730 2003.818431 77.130474 59.385949 \n", + "1 7.566038 2003.745283 92.452830 60.377358 \n", + "2 6.770701 2003.910828 158.184713 26.242038 \n", + "3 7.137540 2003.805016 117.737864 45.135922 \n", + "4 6.929825 2003.820175 86.078947 89.504386 \n", + "\n", + " PRODUCTLINE_Classic Cars ... COUNTRY_Italy COUNTRY_Japan \\\n", + "Cluster ... \n", + "0 0.298358 ... 0.000000 0.024635 \n", + "1 0.198113 ... 1.000000 0.000000 \n", + "2 0.617834 ... 0.044586 0.019108 \n", + "3 0.422330 ... 0.000000 0.013754 \n", + "4 0.000000 ... 0.000000 0.021930 \n", + "\n", + " COUNTRY_Norway COUNTRY_Philippines COUNTRY_Singapore \\\n", + "Cluster \n", + "0 0.034672 0.010036 0.032847 \n", + "1 0.000000 0.000000 0.000000 \n", + "2 0.031847 0.006369 0.025478 \n", + "3 0.033981 0.011327 0.028317 \n", + "4 0.000000 0.000000 0.017544 \n", + "\n", + " COUNTRY_Spain COUNTRY_Sweden COUNTRY_Switzerland COUNTRY_UK \\\n", + "Cluster \n", + "0 0.120438 0.018248 0.008212 0.052007 \n", + "1 0.000000 0.000000 0.000000 0.000000 \n", + "2 0.108280 0.012739 0.000000 0.025478 \n", + "3 0.124595 0.020227 0.017799 0.048544 \n", + "4 0.171053 0.043860 0.000000 0.100877 \n", + "\n", + " COUNTRY_USA \n", + "Cluster \n", + "0 0.359489 \n", + "1 0.000000 \n", + "2 0.407643 \n", + "3 0.385113 \n", + "4 0.307018 \n", + "\n", + "[5 rows x 38 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.groupby(['Cluster']).mean() #Grouping by Cluster\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43d73f00", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Heatmap after Kmeans clustering\n", + "plt.figure(figsize = (20, 20))\n", + "corr_matrix = df.iloc[:, :8].corr()\n", + "sns.heatmap(corr_matrix, annot=True);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fab29ed2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.8 ('3': venv)", + "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": "3.10.8" + }, + "vscode": { + "interpreter": { + "hash": "30169576f97bbc511108375109808e2d217fd6a88f0e16324d22f57023d063d9" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}