{ "cells": [ { "cell_type": "markdown", "id": "60359131", "metadata": {}, "source": [ "وارد کردن کردن کتاب خانه ها " ] }, { "cell_type": "code", "execution_count": 36, "id": "0b7d48a0", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "id": "d938f0d4", "metadata": {}, "source": [ "خواندن دیتاست با استفاده از کتاب خانه پانداس" ] }, { "cell_type": "code", "execution_count": 2, "id": "ff679af6", "metadata": {}, "outputs": [], "source": [ "# Data Frame\n", "df = pd.read_csv('Human Feature.csv')" ] }, { "cell_type": "markdown", "id": "ec62d1f7", "metadata": {}, "source": [ "نمایش اولیه داده ها" ] }, { "cell_type": "code", "execution_count": 6, "id": "1a1182c4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
GenderHeightWeightFootSize
011788243
101625838
211859044
301585237
411707541
\n", "
" ], "text/plain": [ " Gender Height Weight FootSize\n", "0 1 178 82 43\n", "1 0 162 58 38\n", "2 1 185 90 44\n", "3 0 158 52 37\n", "4 1 170 75 41" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "id": "ca34f70e", "metadata": {}, "source": [ "توضیح اولیه داده ها مانند برسی عدد بودن ستون ها" ] }, { "cell_type": "code", "execution_count": 9, "id": "95e75d6b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 100 entries, 0 to 99\n", "Data columns (total 4 columns):\n", " # Column Non-Null Count Dtype\n", "--- ------ -------------- -----\n", " 0 Gender 100 non-null int64\n", " 1 Height 100 non-null int64\n", " 2 Weight 100 non-null int64\n", " 3 FootSize 100 non-null int64\n", "dtypes: int64(4)\n", "memory usage: 3.3 KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "id": "c55801cc", "metadata": {}, "source": [ "برسی تعداد مقادیر خالی در ستون ها " ] }, { "cell_type": "code", "execution_count": 15, "id": "69e850ca", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Gender 0\n", "Height 0\n", "Weight 0\n", "FootSize 0\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isna().sum()" ] }, { "cell_type": "markdown", "id": "923a5895", "metadata": {}, "source": [ "ساخت ورودی مدل هوش مصنوعی با پاک کردن ستون خروجی از تمام داده ها " ] }, { "cell_type": "code", "execution_count": 37, "id": "8cf6f9cd", "metadata": {}, "outputs": [], "source": [ "X = df.drop('Weight',axis=1)" ] }, { "cell_type": "markdown", "id": "4e784326", "metadata": {}, "source": [ "نمایش داده های ورودی برای چک کردن" ] }, { "cell_type": "code", "execution_count": 20, "id": "c6d2f81a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
GenderHeightFootSize
0117843
1016238
2118544
3015837
4117041
\n", "
" ], "text/plain": [ " Gender Height FootSize\n", "0 1 178 43\n", "1 0 162 38\n", "2 1 185 44\n", "3 0 158 37\n", "4 1 170 41" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.head()" ] }, { "cell_type": "markdown", "id": "5f9644dc", "metadata": {}, "source": [ "ساخت خروجی مدل با استفاده از انتخواب ستون از داده های اصلی" ] }, { "cell_type": "code", "execution_count": 21, "id": "a1dc3482", "metadata": {}, "outputs": [], "source": [ "y = df['Weight']" ] }, { "cell_type": "markdown", "id": "b87ed231", "metadata": {}, "source": [ "نمایش داده خروجی" ] }, { "cell_type": "code", "execution_count": 22, "id": "23101b77", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 82\n", "1 58\n", "2 90\n", "3 52\n", "4 75\n", "Name: Weight, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.head()" ] }, { "cell_type": "markdown", "id": "13947458", "metadata": {}, "source": [ "جدا سازی داده ها برای بخش آموزش و بخش تست مدل" ] }, { "cell_type": "code", "execution_count": 35, "id": "3b082d45", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" ] }, { "cell_type": "markdown", "id": "a246bbbc", "metadata": {}, "source": [ "ساخت مدل هوش مصنوعی " ] }, { "cell_type": "code", "execution_count": 38, "id": "80c19f6d", "metadata": {}, "outputs": [], "source": [ "from sklearn import linear_model" ] }, { "cell_type": "code", "execution_count": 39, "id": "8bbd4523", "metadata": {}, "outputs": [], "source": [ "model = linear_model.LinearRegression()" ] }, { "cell_type": "code", "execution_count": 40, "id": "44bd96b9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
LinearRegression()
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": [ "LinearRegression()" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 42, "id": "7d1cc990", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9924658490207758\n" ] } ], "source": [ "print(model.score(X_test, y_test))" ] }, { "cell_type": "code", "execution_count": 44, "id": "fa1247f6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\Khalilavi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:2691: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "array([85.91111531])" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict([[1, 182, 45]])" ] } ], "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": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 5 }