Tree de Decision Simple en Python par IBM Cloud --Bruno Casaca

DecisionTreeSimple.ipynb

{
    "cells": [
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#install graphviz for tree visualization\n!pip install graphviz",
            "execution_count": 1,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n  from cryptography.utils import int_from_bytes\n/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n  from cryptography.utils import int_from_bytes\nCollecting graphviz\n  Downloading graphviz-0.16-py2.py3-none-any.whl (19 kB)\nInstalling collected packages: graphviz\nSuccessfully installed graphviz-0.16\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#import required libraries\nfrom sklearn.tree import DecisionTreeClassifier \nfrom sklearn import tree \nfrom sklearn.preprocessing import LabelEncoder \nfrom sklearn.tree import export_graphviz\nfrom graphviz import Source\nfrom IPython.display import SVG\nimport pandas as pd \nimport numpy as np \nimport io\nimport requests",
            "execution_count": 2,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#Read training Dataset \nurl=\"https://raw.githubusercontent.com/dinasayed/mavenFirst/master/mavenFirst/PlayerFittness.csv\"\ndf=pd.read_csv(url)\nprint(df)",
            "execution_count": 3,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "   Practice Abid_Dietary IsMotivated IsFit\n0  Moderate          Yes         Yes   Yes\n1      good           No         Yes   Yes\n2        No           No         Yes    No\n3  Moderate           No          No    No\n4  Moderate           No         Yes   Yes\n5  Moderate           No         Yes   Yes\n6        No          Yes          No    No\n7      good          Yes          No    No\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Prepare your dataset \nlb = LabelEncoder() \ndf['Practice_'] = lb.fit_transform(df['Practice']) \ndf['Abid_Dietary_'] = lb.fit_transform(df['Abid_Dietary'] ) \ndf['IsMotivated_'] = lb.fit_transform(df['IsMotivated'] )  \ndf['IsFit_'] = lb.fit_transform(df['IsFit'] ) \nX = df.iloc[:,4:7] \nY = df.iloc[:,7]\nprint(\"Features in numeric form\")\nprint(X)\nprint(\"*********************************************\")\nprint(\"Label in numeric form\")\nprint(Y)\n# Prepare a testcase\nX_test=[[0 for x in range(3)] for y in range(1)]\nX_test[0][0]=2\nX_test[0][1]=1\nX_test[0][2]=1\n#Train a Decision tree\nd_tree = DecisionTreeClassifier(criterion='entropy')\nd_tree.fit(X.astype(int), Y.astype(int))\n#Test using sample test X_test\ny_pred_en = d_tree.predict(X_test)\n#The prediction for X-test\nprint(\"The prediction result for test case, given features are: practice is moderate, Abid_Dietary is yes and  IsMotivated is yes\")\nprint(y_pred_en)\n\n",
            "execution_count": null,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#Visualize the decision tree\ngraph = Source(export_graphviz(d_tree, out_file=None, feature_names=X.columns))\nSVG(graph.pipe(format='svg'))",
            "execution_count": null,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "",
            "execution_count": null,
            "outputs": []
        }
    ],
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            "display_name": "Python 3.7",
            "language": "python"
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