mirror of
https://github.com/metafy-social/python-scripts.git
synced 2024-12-18 16:20:17 +00:00
335 lines
99 KiB
Plaintext
335 lines
99 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "05f41765",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas\n",
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"from pandas import DataFrame\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.linear_model import LinearRegression"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "7f82f718",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pandas.read_csv('cost_revenue_clean.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"id": "656efe3f",
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"metadata": {},
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"outputs": [],
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"source": [
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"x= DataFrame(data, columns = ['production_budget_usd'])\n",
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"y= DataFrame(data, columns = ['worldwide_gross_usd'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"id": "6db0a71e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 720x432 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.figure(figsize=(10,6))\n",
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"plt.scatter(x,y,alpha=.4)\n",
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"plt.xlim(0,4.5e8)\n",
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"plt.ylim(0,3e9)\n",
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"plt.xlabel(\"Budget\")\n",
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"plt.ylabel(\"Revenue\")\n",
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"plt.title(\"Cost v/s Revenue\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"id": "ec3fb414",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LinearRegression()"
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]
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},
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"execution_count": 38,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# y = theta_0 + theta_1*X\n",
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"rgr = LinearRegression()\n",
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"rgr.fit(x,y)"
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]
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},
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{
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"cell_type": "code",
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|
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"execution_count": 39,
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"id": "40be1d77",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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|
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"array([[3.11150918]])"
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]
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},
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"execution_count": 39,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#theta_1 or slope\n",
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"th0= rgr.coef_ \n",
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"th0"
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]
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},
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{
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"cell_type": "code",
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|
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"execution_count": 40,
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"id": "e4c31635",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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|
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>production_budget_usd</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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|
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" <td>1000000</td>\n",
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|
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" </tr>\n",
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|
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" <tr>\n",
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|
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" <th>1</th>\n",
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" <td>10000</td>\n",
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|
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" </tr>\n",
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|
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" <tr>\n",
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|
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" <th>2</th>\n",
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" <td>400000</td>\n",
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|
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" </tr>\n",
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|
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" <tr>\n",
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|
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" <th>3</th>\n",
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|
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" <td>750000</td>\n",
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|
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" </tr>\n",
|
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|
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" <tr>\n",
|
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|
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" <th>4</th>\n",
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|
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" <td>10000</td>\n",
|
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|
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" </tr>\n",
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|
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" <tr>\n",
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|
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" <th>...</th>\n",
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|
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" <td>...</td>\n",
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|
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" </tr>\n",
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|
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" <tr>\n",
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|
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" <th>5029</th>\n",
|
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|
|
" <td>225000000</td>\n",
|
|||
|
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" </tr>\n",
|
|||
|
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" <tr>\n",
|
|||
|
|
" <th>5030</th>\n",
|
|||
|
|
" <td>215000000</td>\n",
|
|||
|
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" </tr>\n",
|
|||
|
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" <tr>\n",
|
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|
|
" <th>5031</th>\n",
|
|||
|
|
" <td>306000000</td>\n",
|
|||
|
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" </tr>\n",
|
|||
|
|
" <tr>\n",
|
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|
|
" <th>5032</th>\n",
|
|||
|
|
" <td>200000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5033</th>\n",
|
|||
|
|
" <td>425000000</td>\n",
|
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|
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" </tr>\n",
|
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|
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" </tbody>\n",
|
|||
|
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"</table>\n",
|
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|
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"<p>5034 rows × 1 columns</p>\n",
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|
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"</div>"
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],
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"text/plain": [
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|
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" production_budget_usd\n",
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|
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"0 1000000\n",
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"1 10000\n",
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"2 400000\n",
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|
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"3 750000\n",
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"4 10000\n",
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"... ...\n",
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|
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"5029 225000000\n",
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|
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"5030 215000000\n",
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|
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"5031 306000000\n",
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|
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"5032 200000000\n",
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"5033 425000000\n",
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"\n",
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"[5034 rows x 1 columns]"
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]
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},
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|
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"execution_count": 40,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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|
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"#theta_0 or c\n",
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"th1 = rgr.intercept_\n",
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"th1\n",
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"Y=rgr.predict(x)\n",
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"x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"id": "0d71ef51",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 720x432 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(10,6))\n",
|
|||
|
|
"plt.scatter(x,y,alpha=.3)\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.scatter(x,Y) \n",
|
|||
|
|
"plt.plot(x['production_budget_usd'],Y,color='black') #to convert dataframe to an array \n",
|
|||
|
|
"plt.xlim(0,450000000)\n",
|
|||
|
|
"plt.ylim(0,3000000000)\n",
|
|||
|
|
"plt.xlabel(\"Budget\")\n",
|
|||
|
|
"plt.ylabel(\"Revenue\")\n",
|
|||
|
|
"plt.title(\"Cost v/s Revenue\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 49,
|
|||
|
|
"id": "ba3aa345",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"numpy.ndarray"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 49,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"type(Y)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"id": "49344ba8",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"id": "c8d5afb5",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"id": "c10e871e",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"id": "ad6ae4a1",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3 (ipykernel)",
|
|||
|
|
"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": "3.9.12"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 5
|
|||
|
|
}
|