From 4abddf1dc90140cbfddc5c48cc41485e37fa0b1c Mon Sep 17 00:00:00 2001 From: Manasi Chhibber <68627617+Manasi2001@users.noreply.github.com> Date: Sun, 9 Oct 2022 13:57:28 +0530 Subject: [PATCH] Add files via upload --- .../Dataset/concrete_data.csv | 1031 ++++++++++++++ .../GUI/app.ipynb | 247 ++++ .../Model/Concrete Strength Prediction.ipynb | 1236 +++++++++++++++++ .../Model/ConcreteStrengthPrediction.pkl | Bin 0 -> 500 bytes .../concrete_strength_prediction/README.md | 30 + 5 files changed, 2544 insertions(+) create mode 100644 ML_simple_project/concrete_strength_prediction/Dataset/concrete_data.csv create mode 100644 ML_simple_project/concrete_strength_prediction/GUI/app.ipynb create mode 100644 ML_simple_project/concrete_strength_prediction/Model/Concrete Strength Prediction.ipynb create mode 100644 ML_simple_project/concrete_strength_prediction/Model/ConcreteStrengthPrediction.pkl create mode 100644 ML_simple_project/concrete_strength_prediction/README.md diff --git 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,144.7 ,0.0 ,127.3 ,8.0 ,999.7 ,822.2 ,28 ,44.52 +145.7 ,172.6 ,0.0 ,181.9 ,3.4 ,985.8 ,816.8 ,28 ,23.74 +143.8 ,136.3 ,106.2 ,178.1 ,7.5 ,941.5 ,774.3 ,28 ,26.15 +148.1 ,0.0 ,182.1 ,181.4 ,15.0 ,838.9 ,884.3 ,28 ,15.53 +277.0 ,116.8 ,91.0 ,190.6 ,7.0 ,946.5 ,665.6 ,28 ,43.58 +298.1 ,0.0 ,107.5 ,163.6 ,12.8 ,953.2 ,784.0 ,28 ,35.87 +313.3 ,145.0 ,0.0 ,178.5 ,8.0 ,1001.9 ,688.7 ,28 ,41.05 +155.2 ,183.9 ,143.2 ,193.8 ,9.2 ,879.6 ,698.5 ,28 ,28.99 +289.0 ,133.7 ,0.0 ,194.9 ,5.5 ,924.1 ,760.1 ,28 ,46.25 +147.8 ,175.1 ,0.0 ,171.2 ,2.2 ,1000.0 ,828.5 ,28 ,26.92 +145.4 ,0.0 ,178.9 ,201.7 ,7.8 ,824.0 ,868.7 ,28 ,10.54 +312.7 ,0.0 ,0.0 ,178.1 ,8.0 ,999.7 ,822.2 ,28 ,25.10 +136.4 ,161.6 ,125.8 ,171.6 ,10.4 ,922.6 ,764.4 ,28 ,29.07 +154.8 ,0.0 ,142.8 ,193.3 ,9.1 ,877.2 ,867.7 ,28 ,9.74 +255.3 ,98.8 ,77.0 ,188.6 ,6.5 ,919.0 ,749.3 ,28 ,33.80 +272.8 ,105.1 ,81.8 ,209.7 ,9.0 ,904.0 ,679.7 ,28 ,37.17 +162.0 ,190.1 ,148.1 ,178.8 ,18.8 ,838.1 ,741.4 ,28 ,33.76 +153.6 ,144.2 ,112.3 ,220.1 ,10.1 ,923.2 ,657.9 ,28 ,16.50 +146.5 ,114.6 ,89.3 ,201.9 ,8.8 ,860.0 ,829.5 ,28 ,19.99 +151.8 ,178.1 ,138.7 ,167.5 ,18.3 ,944.0 ,694.6 ,28 ,36.35 +309.9 ,142.8 ,111.2 ,167.8 ,22.1 ,913.9 ,651.2 ,28 ,38.22 +143.6 ,0.0 ,174.9 ,158.4 ,17.9 ,942.7 ,844.5 ,28 ,15.42 +303.6 ,139.9 ,0.0 ,213.5 ,6.2 ,895.5 ,722.5 ,28 ,33.42 +374.3 ,0.0 ,0.0 ,190.2 ,6.7 ,1013.2 ,730.4 ,28 ,39.06 +158.6 ,148.9 ,116.0 ,175.1 ,15.0 ,953.3 ,719.7 ,28 ,27.68 +152.6 ,238.7 ,0.0 ,200.0 ,6.3 ,1001.8 ,683.9 ,28 ,26.86 +310.0 ,142.8 ,0.0 ,167.9 ,10.0 ,914.3 ,804.0 ,28 ,45.30 +304.8 ,0.0 ,99.6 ,196.0 ,9.8 ,959.4 ,705.2 ,28 ,30.12 +150.9 ,0.0 ,183.9 ,166.6 ,11.6 ,991.2 ,772.2 ,28 ,15.57 +141.9 ,166.6 ,129.7 ,173.5 ,10.9 ,882.6 ,785.3 ,28 ,44.61 +297.8 ,137.2 ,106.9 ,201.3 ,6.0 ,878.4 ,655.3 ,28 ,53.52 +321.3 ,164.2 ,0.0 ,190.5 ,4.6 ,870.0 ,774.0 ,28 ,57.22 +366.0 ,187.0 ,0.0 ,191.3 ,6.6 ,824.3 ,756.9 ,28 ,65.91 +279.8 ,128.9 ,100.4 ,172.4 ,9.5 ,825.1 ,804.9 ,28 ,52.83 +252.1 ,97.1 ,75.6 ,193.8 ,8.3 ,835.5 ,821.4 ,28 ,33.40 +164.6 ,0.0 ,150.4 ,181.6 ,11.7 ,1023.3 ,728.9 ,28 ,18.03 +155.6 ,243.5 ,0.0 ,180.3 ,10.7 ,1022.0 ,697.7 ,28 ,37.36 +160.2 ,188.0 ,146.4 ,203.2 ,11.3 ,828.7 ,709.7 ,28 ,35.31 +298.1 ,0.0 ,107.0 ,186.4 ,6.1 ,879.0 ,815.2 ,28 ,42.64 +317.9 ,0.0 ,126.5 ,209.7 ,5.7 ,860.5 ,736.6 ,28 ,40.06 +287.3 ,120.5 ,93.9 ,187.6 ,9.2 ,904.4 ,695.9 ,28 ,43.80 +325.6 ,166.4 ,0.0 ,174.0 ,8.9 ,881.6 ,790.0 ,28 ,61.24 +355.9 ,0.0 ,141.6 ,193.3 ,11.0 ,801.4 ,778.4 ,28 ,40.87 +132.0 ,206.5 ,160.9 ,178.9 ,5.5 ,866.9 ,735.6 ,28 ,33.31 +322.5 ,148.6 ,0.0 ,185.8 ,8.5 ,951.0 ,709.5 ,28 ,52.43 +164.2 ,0.0 ,200.1 ,181.2 ,12.6 ,849.3 ,846.0 ,28 ,15.09 +313.8 ,0.0 ,112.6 ,169.9 ,10.1 ,925.3 ,782.9 ,28 ,38.46 +321.4 ,0.0 ,127.9 ,182.5 ,11.5 ,870.1 ,779.7 ,28 ,37.27 +139.7 ,163.9 ,127.7 ,236.7 ,5.8 ,868.6 ,655.6 ,28 ,35.23 +288.4 ,121.0 ,0.0 ,177.4 ,7.0 ,907.9 ,829.5 ,28 ,42.14 +298.2 ,0.0 ,107.0 ,209.7 ,11.1 ,879.6 ,744.2 ,28 ,31.88 +264.5 ,111.0 ,86.5 ,195.5 ,5.9 ,832.6 ,790.4 ,28 ,41.54 +159.8 ,250.0 ,0.0 ,168.4 ,12.2 ,1049.3 ,688.2 ,28 ,39.46 +166.0 ,259.7 ,0.0 ,183.2 ,12.7 ,858.8 ,826.8 ,28 ,37.92 +276.4 ,116.0 ,90.3 ,179.6 ,8.9 ,870.1 ,768.3 ,28 ,44.28 +322.2 ,0.0 ,115.6 ,196.0 ,10.4 ,817.9 ,813.4 ,28 ,31.18 +148.5 ,139.4 ,108.6 ,192.7 ,6.1 ,892.4 ,780.0 ,28 ,23.70 +159.1 ,186.7 ,0.0 ,175.6 ,11.3 ,989.6 ,788.9 ,28 ,32.77 +260.9 ,100.5 ,78.3 ,200.6 ,8.6 ,864.5 ,761.5 ,28 ,32.40 \ No newline at end of file diff --git a/ML_simple_project/concrete_strength_prediction/GUI/app.ipynb b/ML_simple_project/concrete_strength_prediction/GUI/app.ipynb new file mode 100644 index 0000000..91a3209 --- /dev/null +++ b/ML_simple_project/concrete_strength_prediction/GUI/app.ipynb @@ -0,0 +1,247 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "model = joblib.load(r'C:\\Users\\DELL\\Desktop\\Kaggle+HE\\Github GSSoC21\\Concrete Strength Prediction TBD\\Model\\ConcreteStrengthPrediction.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GUI" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import tkinter as tk \n", + "from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "root = tk.Tk()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "canvas1 = tk.Canvas(root, width = 500, height = 300)\n", + "canvas1.pack()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "heading = tk.Label(root, text='CONCRETE STRENGTH PREDICTION', justify = 'center')\n", + "canvas1.create_window(230, 50, window=heading)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# with sklearn\n", + "Intercept_result = ('Intercept: ', model.intercept_)\n", + "label_Intercept = tk.Label(root, text=Intercept_result, justify = 'center')\n", + "canvas1.create_window(260, 220, window=label_Intercept)\n", + "\n", + "# with sklearn\n", + "Coefficients_result = ('Coefficients: ', model.coef_)\n", + "label_Coefficients = tk.Label(root, text=Coefficients_result, justify = 'center')\n", + "canvas1.create_window(260, 240, window=label_Coefficients)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Blast Furnace Slag label and input box\n", + "label1 = tk.Label(root, text='Blast Furnace Slag: ', justify = 'left')\n", + "canvas1.create_window(100, 100, window=label1)\n", + "\n", + "entry1 = tk.Entry (root) # 1st entry box\n", + "canvas1.create_window(300, 100, window=entry1)\n", + "\n", + "# Fly Ash label and input box\n", + "label2 = tk.Label(root, text='Fly Ash: ', justify = 'left')\n", + "canvas1.create_window(100, 120, window=label2)\n", + "\n", + "entry2 = tk.Entry (root) # 2nd entry box\n", + "canvas1.create_window(300, 120, window=entry2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Superplasticizer label and input box\n", + "label3 = tk.Label(root, text='Superplasticizer: ', justify = 'left')\n", + "canvas1.create_window(100, 140, window=label3)\n", + "\n", + "entry3 = tk.Entry (root) # 3rd entry box\n", + "canvas1.create_window(300, 140, window=entry3)\n", + "\n", + "# Age label and input box\n", + "label4 = tk.Label(root, text='Age: ', justify = 'left')\n", + "canvas1.create_window(100, 160, window=label4)\n", + "\n", + "entry4 = tk.Entry (root) # 4th entry box\n", + "canvas1.create_window(300, 160, window=entry4)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def values(): \n", + " global bfs # our 1st input variable\n", + " bfs = float(entry1.get()) \n", + " \n", + " global fa # our 2nd input variable\n", + " fa = float(entry2.get()) \n", + "\n", + " global s # our 3rd input variable\n", + " s = float(entry3.get())\n", + "\n", + " global a # our 4th input variable\n", + " a = float(entry4.get())\n", + " \n", + " Prediction_result = ('Predicted Concrete Strength: ', model.predict([[bfs, fa, s, a]]))\n", + " label_Prediction = tk.Label(root, text= Prediction_result, bg='pink')\n", + " canvas1.create_window(240, 280, window=label_Prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "button1 = tk.Button (root, text='Predict Concrete Strength',command=values, bg='pink') # button to call the 'values' command above \n", + "canvas1.create_window(230, 200, window=button1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "root.configure(bg='white')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "root.mainloop()" + ] + } + ], + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ML_simple_project/concrete_strength_prediction/Model/Concrete Strength Prediction.ipynb b/ML_simple_project/concrete_strength_prediction/Model/Concrete Strength Prediction.ipynb new file mode 100644 index 0000000..debe6a2 --- /dev/null +++ b/ML_simple_project/concrete_strength_prediction/Model/Concrete Strength Prediction.ipynb @@ -0,0 +1,1236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CONCRETE STRENGTH PREDICTION" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sb\n", + "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import r2_score\n", + "from sklearn.metrics import mean_squared_error" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting our Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CementBlast Furnace SlagFly AshWaterSuperplasticizerCoarse AggregateFine AggregateAgeStrength
0540.00.00.0162.02.51040.0676.02879.99
1540.00.00.0162.02.51055.0676.02861.89
2332.5142.50.0228.00.0932.0594.027040.27
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4198.6132.40.0192.00.0978.4825.536044.30
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1025276.4116.090.3179.68.9870.1768.32844.28
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1030 rows × 9 columns

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" + ], + "text/plain": [ + " Cement Blast Furnace Slag Fly Ash Water Superplasticizer \\\n", + "0 540.0 0.0 0.0 162.0 2.5 \n", + "1 540.0 0.0 0.0 162.0 2.5 \n", + "2 332.5 142.5 0.0 228.0 0.0 \n", + "3 332.5 142.5 0.0 228.0 0.0 \n", + "4 198.6 132.4 0.0 192.0 0.0 \n", + "... ... ... ... ... ... \n", + "1025 276.4 116.0 90.3 179.6 8.9 \n", + "1026 322.2 0.0 115.6 196.0 10.4 \n", + "1027 148.5 139.4 108.6 192.7 6.1 \n", + "1028 159.1 186.7 0.0 175.6 11.3 \n", + "1029 260.9 100.5 78.3 200.6 8.6 \n", + "\n", + " Coarse Aggregate Fine Aggregate Age Strength \n", + "0 1040.0 676.0 28 79.99 \n", + "1 1055.0 676.0 28 61.89 \n", + "2 932.0 594.0 270 40.27 \n", + "3 932.0 594.0 365 41.05 \n", + "4 978.4 825.5 360 44.30 \n", + "... ... ... ... ... \n", + "1025 870.1 768.3 28 44.28 \n", + "1026 817.9 813.4 28 31.18 \n", + "1027 892.4 780.0 28 23.70 \n", + "1028 989.6 788.9 28 32.77 \n", + "1029 864.5 761.5 28 32.40 \n", + "\n", + "[1030 rows x 9 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(r'C:\\Users\\DELL\\Desktop\\Kaggle+HE\\Github GSSoC21\\Concrete Strength Prediction TBD\\Dataset\\concrete_data.csv')\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Cement False\n", + "Blast Furnace Slag False\n", + "Fly Ash False\n", + "Water False\n", + "Superplasticizer False\n", + "Coarse Aggregate False\n", + "Fine Aggregate False\n", + "Age False\n", + "Strength False\n", + "dtype: bool" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking for null values\n", + "df.isnull().any()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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VIFFeatures
015.456717Cement
13.329127Blast Furnace Slag
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584.955779Coarse Aggregate
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71.699459Age
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" + ], + "text/plain": [ + " VIF Features\n", + "0 15.456717 Cement\n", + "1 3.329127 Blast Furnace Slag\n", + "2 4.147833 Fly Ash\n", + "3 82.157569 Water\n", + "4 5.471094 Superplasticizer\n", + "5 84.955779 Coarse Aggregate\n", + "6 72.790995 Fine Aggregate\n", + "7 1.699459 Age" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking vif\n", + "variables = df[['Cement','Blast Furnace Slag','Fly Ash','Water','Superplasticizer','Coarse Aggregate','Fine Aggregate','Age']]\n", + "vif = pd.DataFrame()\n", + "vif['VIF'] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n", + "vif['Features'] = variables.columns\n", + "\n", + "vif" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Blast Furnace SlagFly AshSuperplasticizerAgeStrength
00.00.02.52879.99
10.00.02.52861.89
2142.50.00.027040.27
3142.50.00.036541.05
4132.40.00.036044.30
..................
1025116.090.38.92844.28
10260.0115.610.42831.18
1027139.4108.66.12823.70
1028186.70.011.32832.77
1029100.578.38.62832.40
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1030 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Blast Furnace Slag Fly Ash Superplasticizer Age Strength\n", + "0 0.0 0.0 2.5 28 79.99\n", + "1 0.0 0.0 2.5 28 61.89\n", + "2 142.5 0.0 0.0 270 40.27\n", + "3 142.5 0.0 0.0 365 41.05\n", + "4 132.4 0.0 0.0 360 44.30\n", + "... ... ... ... ... ...\n", + "1025 116.0 90.3 8.9 28 44.28\n", + "1026 0.0 115.6 10.4 28 31.18\n", + "1027 139.4 108.6 6.1 28 23.70\n", + "1028 186.7 0.0 11.3 28 32.77\n", + "1029 100.5 78.3 8.6 28 32.40\n", + "\n", + "[1030 rows x 5 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now, we'll drop columns which have vif>10\n", + "df = df.drop(['Cement','Water','Coarse Aggregate','Fine Aggregate'], axis=1)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# checking for outliners\n", + "sb.boxplot(x=df['Blast Furnace Slag'])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# removing outliners\n", + "outliers_removed = [x for x in df['Blast Furnace Slag'] if x >= 300 and x <= 400]\n", + "for i in df['Blast Furnace Slag']:\n", + " if i in set(outliers_removed):\n", + " df['Blast Furnace Slag'] = df['Blast Furnace Slag'].replace(i,df['Blast Furnace Slag'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "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": [ + "sb.boxplot(x=df['Blast Furnace Slag'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sb.boxplot(x=df['Superplasticizer'])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# removing outliners\n", + "outliers_removed = [x for x in df['Superplasticizer'] if x >= 25 and x <= 40]\n", + "for i in df['Superplasticizer']:\n", + " if i in set(outliers_removed):\n", + " df['Superplasticizer'] = df['Superplasticizer'].replace(i,df['Superplasticizer'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sb.boxplot(x=df['Superplasticizer'])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sb.boxplot(x=df['Age'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# removing outliners\n", + "outliers_removed = [x for x in df['Age'] if x >= 150 and x <= 400]\n", + "for i in df['Age']:\n", + " if i in set(outliers_removed):\n", + " df['Age'] = df['Age'].replace(i,df['Age'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sb.boxplot(x=df['Age'])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# removing new outliners\n", + "outliers_removed = [x for x in df['Age'] if x >= 60 and x <= 150]\n", + "for i in df['Age']:\n", + " if i in set(outliers_removed):\n", + " df['Age'] = df['Age'].replace(i,df['Age'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
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Blast Furnace SlagFly AshSuperplasticizerAgeStrength
count1030.0000001030.0000001030.0000001030.0000001030.000000
mean71.44262554.1883505.97108324.78616235.817961
std82.56933663.9970045.47703214.78042716.705742
min0.0000000.0000000.0000001.0000002.330000
25%0.0000000.0000000.0000007.00000023.710000
50%22.0000000.0000006.15000028.00000034.445000
75%141.300000118.30000010.07500028.66410246.135000
max290.200000200.10000023.40000056.00000082.600000
\n", + "
" + ], + "text/plain": [ + " Blast Furnace Slag Fly Ash Superplasticizer Age \\\n", + "count 1030.000000 1030.000000 1030.000000 1030.000000 \n", + "mean 71.442625 54.188350 5.971083 24.786162 \n", + "std 82.569336 63.997004 5.477032 14.780427 \n", + "min 0.000000 0.000000 0.000000 1.000000 \n", + "25% 0.000000 0.000000 0.000000 7.000000 \n", + "50% 22.000000 0.000000 6.150000 28.000000 \n", + "75% 141.300000 118.300000 10.075000 28.664102 \n", + "max 290.200000 200.100000 23.400000 56.000000 \n", + "\n", + " Strength \n", + "count 1030.000000 \n", + "mean 35.817961 \n", + "std 16.705742 \n", + "min 2.330000 \n", + "25% 23.710000 \n", + "50% 34.445000 \n", + "75% 46.135000 \n", + "max 82.600000 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking if standardization is required or not\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Blast Furnace Slag'].plot.hist(grid=True, bins=20, rwidth=0.9, color='#607c8e')\n", + "plt.title('Blast Furnace Slag Distribution')\n", + "plt.grid(axis='y', alpha=0.75)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# the data seems to be left skewed; we'll have to check the value of skewness" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Blast Furnace Slag Skewness: 0.7207693210681195\n", + "Fly Ash Skewness: 0.5373539058066844\n", + "Superplasticizer Skewness: 0.4695534757361076\n", + "Age Skewness: 0.2642589721506312\n" + ] + } + ], + "source": [ + "# checking extent of skewness\n", + "print('Blast Furnace Slag Skewness:', df['Blast Furnace Slag'].skew())\n", + "print('Fly Ash Skewness:', df['Fly Ash'].skew())\n", + "print('Superplasticizer Skewness:', df['Superplasticizer'].skew())\n", + "print('Age Skewness:', df['Age'].skew())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# as the skewness in within range of acceptance, we do not need to do any transformations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# using Pearson's correlation method\n", + "corr = df.corr(method='pearson')\n", + "sb.heatmap(corr, annot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Splitting Data for Training and Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "data = df.values\n", + "X,y = data[:,:-1], data[:,-1]\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) # splitting in the ration 80:20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "model = LinearRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Making Predictions and Checking Accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.47194805969559406" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r2_score(y_test, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### With simple Linear Regression, we have achieved 47% accuracy. On applying other algorithms, accuracy of around 80% can be achieved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting the pkl file" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "pickle.dump(model, open('ConcreteStrengthPrediction.pkl', 'wb'))" + ] + } + ], + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ML_simple_project/concrete_strength_prediction/Model/ConcreteStrengthPrediction.pkl b/ML_simple_project/concrete_strength_prediction/Model/ConcreteStrengthPrediction.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b2467a488c47078a7ad316592f05c6df05bb01e2 GIT binary patch literal 500 zcmYk3K}Z`x6o!*_lQvc*lzK2wur~#}RD0>6kcHg38mI^sEDW=oiMw?(6J};pwoqsh ztAU3XUk?RAK~J897Q6_BUOWnV^4yCRq?cAK6w=8m(s!7FH~)Lz|K`23KK{sN(twhh zj~JEQjzVs3(hxNk*~FuYArvRl5xiE z6NFwOnJ>7~GWNA$LoO8}+~T!M$tt>;MXY2!T*4_FcC)Uvh(SN(YC37MZpn@M6KpSE zzTSlFQ><32&pIa3!_8>)+LfV;1@GbZ!t?S`NBc9!yI*$(22Wq#KQFIMeinb$zL(L+ zpkVB%uog3g!5zpepGH&~Mo_BUep&oA+uHi;;0NOaa+31-bi{%R`CM#t1cSby*mWD+ z{oiZJ&W9#6P^JRa++WVb2*3Z$6dp31ade8N5n^q6H%x_~u>nn2X ZidiIAt0XNZ(`7;akv;y{9>>_T{{bB^&tm`p literal 0 HcmV?d00001 diff --git a/ML_simple_project/concrete_strength_prediction/README.md b/ML_simple_project/concrete_strength_prediction/README.md new file mode 100644 index 0000000..d6528bd --- /dev/null +++ b/ML_simple_project/concrete_strength_prediction/README.md @@ -0,0 +1,30 @@ +# CONCRETE STRENGTH PREDICTION + +## Content + +The feature set includes: + +Cement, +Blast Furnace Slag, +Fly Ash, +Water, +Super-plasticizer, +Coarse Aggregate, +Fine Aggregate and +Age + +The target set is: + +Strength of the Cement + +## ML Algorithm Used + +Linear Regression + +## Package Used for GUI + +tkinter + +## Source + +https://www.kaggle.com/prathamtripathi/regression-with-neural-networking