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andisugandi/FilmCost-v.s-GlobalRevenue.ipynb

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FilmCost-v.s-GlobalRevenue.ipynb
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FilmCost-v.s-GlobalRevenue.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "01 Linear Regression.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyNVHhEvf9p8LqyDktSbDn4+",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/andisugandi/5a39a2a0799bb372fdcf4a9216dcaff1/01-linear-regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tmplgWDwTvAc"
},
"source": [
"import pandas\n",
"from pandas import DataFrame\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from sklearn.linear_model import LinearRegression"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "WZM96ENLUouB"
},
"source": [
"# Load the dataset (clean data)\n",
"data = pandas.read_csv('cost_revenue_clean.csv')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"id": "tmShKg8bUxcL",
"outputId": "0b31bc82-defa-4882-92c0-765f24b71340"
},
"source": [
"data"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>production_budget_usd</th>\n",
" <th>worldwide_gross_usd</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1000000</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10000</td>\n",
" <td>401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>400000</td>\n",
" <td>423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>750000</td>\n",
" <td>450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10000</td>\n",
" <td>527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5029</th>\n",
" <td>225000000</td>\n",
" <td>1519479547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5030</th>\n",
" <td>215000000</td>\n",
" <td>1671640593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5031</th>\n",
" <td>306000000</td>\n",
" <td>2058662225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5032</th>\n",
" <td>200000000</td>\n",
" <td>2207615668</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5033</th>\n",
" <td>425000000</td>\n",
" <td>2783918982</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5034 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" production_budget_usd worldwide_gross_usd\n",
"0 1000000 26\n",
"1 10000 401\n",
"2 400000 423\n",
"3 750000 450\n",
"4 10000 527\n",
"... ... ...\n",
"5029 225000000 1519479547\n",
"5030 215000000 1671640593\n",
"5031 306000000 2058662225\n",
"5032 200000000 2207615668\n",
"5033 425000000 2783918982\n",
"\n",
"[5034 rows x 2 columns]"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"id": "Hpi3gPwoU-1X",
"outputId": "67f2e836-cc1d-4180-c7e8-984b4a53edcd"
},
"source": [
"data.describe()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>production_budget_usd</th>\n",
" <th>worldwide_gross_usd</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>5.034000e+03</td>\n",
" <td>5.034000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.290784e+07</td>\n",
" <td>9.515685e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>4.112589e+07</td>\n",
" <td>1.726012e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.100000e+03</td>\n",
" <td>2.600000e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>6.000000e+06</td>\n",
" <td>7.000000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.900000e+07</td>\n",
" <td>3.296202e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>4.200000e+07</td>\n",
" <td>1.034471e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4.250000e+08</td>\n",
" <td>2.783919e+09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" production_budget_usd worldwide_gross_usd\n",
"count 5.034000e+03 5.034000e+03\n",
"mean 3.290784e+07 9.515685e+07\n",
"std 4.112589e+07 1.726012e+08\n",
"min 1.100000e+03 2.600000e+01\n",
"25% 6.000000e+06 7.000000e+06\n",
"50% 1.900000e+07 3.296202e+07\n",
"75% 4.200000e+07 1.034471e+08\n",
"max 4.250000e+08 2.783919e+09"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "B9OMIw4XVIfF"
},
"source": [
"# Define the data/predictiors as the pre-set feature names.\n",
"x = DataFrame(data, columns=['production_budget_usd'])\n",
"\n",
"# Put the target in another DataFrame.\n",
"y = DataFrame(data, columns=['worldwide_gross_usd'])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5BzirakeXjNp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5f69350d-571d-4b6b-ddee-70874df0b4f9"
},
"source": [
"regression = LinearRegression()\n",
"regression.fit(x, y)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 567
},
"id": "m9EB0DqxWaow",
"outputId": "e62798cd-9d27-49a8-bf68-98f24faab0e5"
},
"source": [
"plt.figure(figsize=(15,9))\n",
"plt.scatter(x,y, alpha=0.3)\n",
"plt.plot(x, regression.predict(x), color='#e74c3c', linewidth=4)\n",
"\n",
"plt.title('Film Cost vs. Global Revenue')\n",
"plt.xlabel('Product Budget $')\n",
"plt.ylabel('Worldside Gross $')\n",
"\n",
"plt.ylim(0, 3000000000)\n",
"plt.xlim(0, 450000000)\n",
"\n",
"plt.style.use('fivethirtyeight')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 1080x648 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mGJ2dUXtHVEp"
},
"source": [
"**Slope Coefficient**:"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iKt4naP_G3u4",
"outputId": "b642c6dc-e637-4950-8e91-66f665aa4830"
},
"source": [
"# theta_1\n",
"print('Theta1 (slope): \\n', regression.coef_)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Theta1 (slope): \n",
" [[3.11150918]]\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Dh6MPYLYHPLs",
"outputId": "064d6f78-14bf-4d52-b459-eb10e5ef56ff"
},
"source": [
"# Print Intercept\n",
"regression.intercept_"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([-7236192.72913958])"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6Kq1UxfDHqoL",
"outputId": "1a5836a1-0a9e-436f-8898-512e78cc3ac8"
},
"source": [
"# Getting r square from regression\n",
"print('R-square', regression.score(x, y))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"R-square 0.5496485356985729\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pJrbrbTTK5JX",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a6551d53-6463-4846-fc3b-01460d563ace"
},
"source": [
"type(regression.intercept_)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FC8PQDZ-7fxB",
"outputId": "cd97beee-2ba2-4012-bf7a-3d6e15596522"
},
"source": [
"regression.intercept_[0]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"-7236192.729139581"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uzyIJcW3KpJV"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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