{ "cells": [ { "cell_type": "code", "execution_count": 78, "id": "836ce925", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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companyjobdegreesalary_more_then_100k
0googlesales executivebachelors0
1googlesales executivemasters0
2googlebusiness managerbachelors1
3googlebusiness managermasters1
4googlecomputer programmerbachelors0
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" ], "text/plain": [ " company job degree salary_more_then_100k\n", "0 google sales executive bachelors 0\n", "1 google sales executive masters 0\n", "2 google business manager bachelors 1\n", "3 google business manager masters 1\n", "4 google computer programmer bachelors 0" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from sklearn import tree, metrics\n", "df = pd.read_csv(\"salaries.csv\")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 79, "id": "ecda5b58", "metadata": {}, "outputs": [], "source": [ "X = df.drop('salary_more_then_100k', axis='columns')\n", "Y = df['salary_more_then_100k']" ] }, { "cell_type": "code", "execution_count": 80, "id": "abdab77f", "metadata": {}, "outputs": [], "source": [ "#import LabelEncoder\n", "from sklearn.preprocessing import LabelEncoder" ] }, { "cell_type": "code", "execution_count": 81, "id": "56976744", "metadata": {}, "outputs": [], "source": [ "#create 3 object using LabelEncoder\n", "con_company = LabelEncoder()\n", "con_job = LabelEncoder()\n", "con_degree = LabelEncoder()" ] }, { "cell_type": "code", "execution_count": 82, "id": "89d55152", "metadata": {}, "outputs": [], "source": [ "#adding three extra column in data\n", "inputs['company_n'] = con_company.fit_transform(inputs['company'])\n", "inputs['job_n'] = con_company.fit_transform(inputs['job'])\n", "inputs['degree_n'] = con_company.fit_transform(inputs['degree'])" ] }, { "cell_type": "code", "execution_count": 83, "id": "84daadc8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inputs.head" ] }, { "cell_type": "code", "execution_count": 84, "id": "e11ec4b0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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company_njob_ndegree_n
0220
1221
2200
3201
4210
5211
6021
7010
8000
9001
10120
11121
12100
13101
14110
15111
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" ], "text/plain": [ " company_n job_n degree_n\n", "0 2 2 0\n", "1 2 2 1\n", "2 2 0 0\n", "3 2 0 1\n", "4 2 1 0\n", "5 2 1 1\n", "6 0 2 1\n", "7 0 1 0\n", "8 0 0 0\n", "9 0 0 1\n", "10 1 2 0\n", "11 1 2 1\n", "12 1 0 0\n", "13 1 0 1\n", "14 1 1 0\n", "15 1 1 1" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#delete column\n", "new_Inputs = inputs.drop(['company','job', 'degree'], axis='columns')\n", "new_Inputs" ] }, { "cell_type": "code", "execution_count": 85, "id": "bb438ccb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ],\n", " [, ]], dtype=object)" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_Inputs.hist(column=['company_n','job_n','degree_n'])" ] }, { "cell_type": "markdown", "id": "a9327809", "metadata": {}, "source": [ "# Decision Tree" ] }, { "cell_type": "code", "execution_count": 86, "id": "1f22ac6f", "metadata": {}, "outputs": [], "source": [ "# Making Model using DTC(DecisionTreeClassifier) \n", "model = tree.DecisionTreeClassifier(random_state=42)" ] }, { "cell_type": "code", "execution_count": 87, "id": "0067db7c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
DecisionTreeClassifier(random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "DecisionTreeClassifier(random_state=42)" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Train your model\n", "model.fit(new_Inputs,Y) # Y is targeted Variable" ] }, { "cell_type": "code", "execution_count": 88, "id": "24d35d26", "metadata": {}, "outputs": [], "source": [ "# for prediction \n", "# is google emp have salary_more_then_100k\n", "\n", "y_pred = model.predict(new_Inputs) # (ex. new_Inputs)" ] }, { "cell_type": "code", "execution_count": 89, "id": "134a113c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 0 1 1 0 1 0 0 0 1 1 1 1 1 1 1]\n" ] } ], "source": [ "print(y_pred)" ] }, { "cell_type": "code", "execution_count": 90, "id": "8ebd2134", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "print(metrics.accuracy_score(Y, y_pred))" ] }, { "cell_type": "code", "execution_count": 91, "id": "579ac432", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 6\n", " 1 1.00 1.00 1.00 10\n", "\n", " accuracy 1.00 16\n", " macro avg 1.00 1.00 1.00 16\n", "weighted avg 1.00 1.00 1.00 16\n", "\n" ] } ], "source": [ "print(metrics.classification_report(Y, y_pred))" ] }, { "cell_type": "code", "execution_count": 92, "id": "995327a2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy With Max Depth 3 is= 0.9375\n", "Accuracy With Max Depth 4 is= 0.9375\n", "Accuracy With Max Depth 5 is= 1.0\n", "Accuracy With Max Depth 6 is= 1.0\n", "Accuracy With Max Depth 7 is= 1.0\n", "Accuracy With Max Depth 8 is= 1.0\n", "Accuracy With Max Depth 9 is= 1.0\n", "Accuracy With Max Depth 10 is= 1.0\n", "Accuracy With Max Depth 11 is= 1.0\n", "Accuracy With Max Depth 12 is= 1.0\n", "Accuracy With Max Depth 13 is= 1.0\n", "Accuracy With Max Depth 14 is= 1.0\n", "Accuracy With Max Depth 15 is= 1.0\n", "Accuracy With Max Depth 16 is= 1.0\n", "Accuracy With Max Depth 17 is= 1.0\n", "Accuracy With Max Depth 18 is= 1.0\n", "Accuracy With Max Depth 19 is= 1.0\n" ] } ], "source": [ "# Check for the best Max_Depth\n", "\n", "for i in range(3, 20):\n", " model_1 = tree.DecisionTreeClassifier(max_depth=i,random_state=2)\n", " model_1.fit(new_Inputs, Y)\n", " y_pred_1 = model_1.predict(new_Inputs)\n", " print('Accuracy With Max Depth', i, 'is= ',metrics.accuracy_score(Y, y_pred_1))" ] } ], "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": "3.13.3" } }, "nbformat": 4, "nbformat_minor": 5 }