702 lines
16 KiB
Plaintext
702 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "xwFyEsosINqT"
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd"
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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": null,
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"metadata": {
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"id": "pKewSQysItJ-"
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},
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"outputs": [],
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"source": [
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"# https://www.statsmodels.org/stable/index.html\n",
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"import statsmodels.api as sm"
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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": null,
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"metadata": {
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"id": "Lz-DyAtNWsJR"
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},
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"outputs": [],
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"source": [
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"# Download Dataset from https://www.dropbox.com/scl/fi/bkcdp9tpqqh6dfr6phtt8/AnnArbor.xlsx?rlkey=0agfqwc7f0kt7oqb3e2h6q3qs&dl=1\n",
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"# and add it to colab"
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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": null,
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"metadata": {
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"id": "0zM8FGMJXJ70"
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},
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"outputs": [],
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"source": [
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"# annArborDf = pd.read_excel(\"./AnnArbor.xlsx\")\n",
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"annArborDf = pd.read_excel(\"https://www.dropbox.com/scl/fi/bkcdp9tpqqh6dfr6phtt8/AnnArbor.xlsx?rlkey=0agfqwc7f0kt7oqb3e2h6q3qs&dl=1\")"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"id": "t0LUca0Myqw5",
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"outputId": "249ab087-895f-4fa6-993e-e8dd50ef87c1"
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},
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"outputs": [],
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"source": [
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"annArborDf"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "GQRNPIeyy6ub",
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"outputId": "00211933-f2b1-40c6-d9cf-187560ffa305"
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},
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"outputs": [],
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"source": [
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"annArborDf.size"
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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": null,
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"metadata": {
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"id": "yumMybniy85d"
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},
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"outputs": [],
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"source": [
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"annArborDf.describe()"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "aspq6hoPy_xZ",
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"outputId": "96892272-a1d5-400e-a177-6c96746619d8"
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},
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"outputs": [],
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"source": [
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"annArborDf.shape"
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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": null,
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"metadata": {
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"id": "z_hVTvPrzYJr"
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"id": "pIniVuaIzaaZ",
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"outputId": "6a061f6a-8bff-42c0-d705-0c2bd06eb5ff"
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},
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"outputs": [],
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"source": [
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"# Plotting\n",
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"fig1 = plt.figure(\n",
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" figsize=(8, 8)\n",
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")"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 449
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},
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"id": "VHdpDE7o42Pf",
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"outputId": "ac876802-b6d1-4926-d069-0532ee9e7a0b"
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},
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"outputs": [],
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"source": [
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"plt.scatter(\n",
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" annArborDf[\"Beds\"],\n",
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" annArborDf[\"Rent\"],\n",
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" color='blue',\n",
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" alpha=0.9,\n",
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" label='Data Points - scatter',\n",
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")\n",
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"\n",
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"plt.xlabel('Beds')\n",
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"plt.ylabel('Rent')\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"\n",
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"\n",
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"\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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 449
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},
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"id": "knAa4W9R47rZ",
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"outputId": "81359d91-03b7-4f70-c381-c88172f800a9"
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},
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"outputs": [],
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"source": [
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"plt.scatter(\n",
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" annArborDf[\"Baths\"],\n",
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" annArborDf[\"Rent\"],\n",
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" color='blue',\n",
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" alpha=0.9,\n",
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" label='Data Points - scatter',\n",
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")\n",
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"\n",
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"plt.xlabel('Baths')\n",
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"plt.ylabel('Rent')\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"\n",
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"\n",
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"\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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 449
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},
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"id": "dOnWJbFOzczV",
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"outputId": "c6d6b86b-dd85-45d1-b543-928441c11dc4"
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},
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"outputs": [],
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"source": [
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"plt.scatter(\n",
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" annArborDf[\"Sqft\"],\n",
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" annArborDf[\"Rent\"],\n",
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" color='blue',\n",
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" alpha=0.9,\n",
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" label='Data Points - scatter',\n",
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")\n",
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"\n",
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"plt.xlabel('Sqft')\n",
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"plt.ylabel('Rent')\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"\n",
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"\n",
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"\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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "alIhUPPUzvli",
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"outputId": "8ed14c4b-a596-49ac-912a-0dcb4145df89"
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},
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"outputs": [],
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"source": [
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"rentSqftModel1 = sm.OLS(\n",
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" annArborDf[\"Rent\"],\n",
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" sm.add_constant(annArborDf[[\"Sqft\"]])\n",
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")\n",
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"rentSqftModel1Fit = rentSqftModel1.fit()\n",
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"print(rentSqftModel1Fit.summary())"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from functions.exportModel import exportModel\n",
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"exportModel({\n",
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" \"modelName\": \"rentSqftModel1Fit\",\n",
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" \"model\": rentSqftModel1Fit,\n",
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" \"description\": \"Predict Rent based on Sqft for annArborDf\",\n",
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" \"modelType\": \"sm.OLS\",\n",
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" \"baseRelativePath\": \"..\",\n",
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" \"inputs\": [\n",
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" {\n",
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" \"name\": \"const\",\n",
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" \"type\": \"int\"\n",
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" },\n",
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" {\n",
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" \"name\": \"Sqft\",\n",
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" \"type\": \"float\"\n",
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" }\n",
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" ],\n",
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" \"output\": {\n",
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" \"name\": \"Rent\",\n",
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" \"type\": \"float\"\n",
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" }\n",
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"})"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"id": "S-AyfiLN0Due",
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"outputId": "aacd248d-5a72-4ce0-ab0a-048f30d398ca"
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},
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"outputs": [],
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"source": [
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"predictedRent1 = rentSqftModel1Fit.predict(sm.add_constant(annArborDf[\"Sqft\"]))\n",
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"annArborDf['predictedRent1'] = predictedRent1\n",
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"annArborDf"
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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": null,
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"metadata": {
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"id": "9ouX-mzz4sl-"
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 454
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},
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"id": "L55GN8hZ4wXi",
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"outputId": "712ace2c-5a04-48e0-acf0-cc42430f2aa9"
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},
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"outputs": [],
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"source": [
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"plt.scatter(\n",
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" annArborDf[\"Rent\"],\n",
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" annArborDf[\"Sqft\"],\n",
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" color='blue',\n",
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" alpha=0.5,\n",
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" label='Data Points - scatter',\n",
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")\n",
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"\n",
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"intercept = rentSqftModel1Fit.params['const']\n",
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"sqFtSlope = rentSqftModel1Fit.params['Sqft']\n",
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"x_values = np.linspace(500, 4500, 200)\n",
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"y_values = intercept + sqFtSlope * x_values\n",
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"\n",
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"plt.plot(\n",
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" x_values,\n",
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" y_values,\n",
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" color='red',\n",
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" label='rentSqftModel1Fit - predictedRent1'\n",
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")\n",
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"plt.xlabel('Sqft')\n",
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"plt.ylabel('Rent')\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"\n",
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"\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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "swSVnmy44Ddg",
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"outputId": "251afab3-0563-4eb7-e23a-b526238c7584"
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},
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"outputs": [],
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"source": [
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"rentBedsBathsSqftModel = sm.OLS(\n",
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" annArborDf[\"Rent\"],\n",
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" sm.add_constant(annArborDf[[\"Beds\", \"Baths\", \"Sqft\"]])\n",
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")\n",
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"rentBedsBathsSqftModelFit = rentBedsBathsSqftModel.fit()\n",
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"print(rentBedsBathsSqftModelFit.summary())"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from functions.exportModel import exportModel\n",
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"exportModel({\n",
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" \"modelName\": \"rentBedsBathsSqftModelFit\",\n",
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" \"model\": rentBedsBathsSqftModelFit,\n",
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" \"description\": \"Predict Rent based on Beds,Baths,Sqft for annArborDf\",\n",
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" \"modelType\": \"sm.OLS\",\n",
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" \"baseRelativePath\": \"..\",\n",
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" \"inputs\": [\n",
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" {\n",
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" \"name\": \"const\",\n",
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" \"type\": \"int\"\n",
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" },\n",
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" {\n",
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" \"name\": \"Beds\",\n",
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" \"type\": \"int\"\n",
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" },\n",
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" {\n",
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" \"name\": \"Baths\",\n",
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" \"type\": \"int\"\n",
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" },\n",
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" {\n",
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" \"name\": \"Sqft\",\n",
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" \"type\": \"float\"\n",
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" }\n",
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" \n",
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" ],\n",
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" \"output\": {\n",
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" \"name\": \"Rent\",\n",
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" \"type\": \"float\"\n",
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" }\n",
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"})"
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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": null,
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"metadata": {
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"id": "6lKEw7Wt57Px"
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},
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"outputs": [],
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"source": [
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"import math"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"id": "da3o51IG5u7r",
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"outputId": "abe849ba-7689-468c-f327-b183c4d3f70a"
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},
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"outputs": [],
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"source": [
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"from functions.transformers import transformersDict\n",
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"# annArborDf['log(Sqft)'] = annArborDf.apply(lambda row: math.log(row['Sqft']), axis=1)\n",
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"annArborDf['log(Sqft)'] = annArborDf.apply(transformersDict.get('Sqft_log'), axis=1)\n",
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"annArborDf"
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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": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "lYYrtI0O5lSG",
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"outputId": "6a980e88-5630-4e5e-f887-875ab5f1d748"
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},
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"outputs": [],
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"source": [
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"rentBedsBathsLogSqftModel= sm.OLS(\n",
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" annArborDf[\"Rent\"],\n",
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" sm.add_constant(annArborDf[[\"Beds\", \"Baths\", \"log(Sqft)\"]])\n",
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")\n",
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"rentBedsBathsLogSqftModelFit = rentBedsBathsLogSqftModel.fit()\n",
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"print(rentBedsBathsLogSqftModelFit.summary())"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from functions.exportModel import exportModel\n",
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"exportModel({\n",
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" \"modelName\": \"rentBedsBathsLogSqftModelFit\",\n",
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" \"model\": rentBedsBathsLogSqftModelFit,\n",
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" \"description\": \"Predict Rent based on Beds,Baths,log(Sqft) for annArborDf\",\n",
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" \"modelType\": \"sm.OLS\",\n",
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" \"baseRelativePath\": \"..\",\n",
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" \"inputs\": [\n",
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" {\n",
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" \"name\": \"const\",\n",
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" \"type\": \"int\"\n",
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" },\n",
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" {\n",
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" \"name\": \"Beds\",\n",
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" \"type\": \"int\"\n",
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" },\n",
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" {\n",
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" \"name\": \"Baths\",\n",
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" \"type\": \"int\"\n",
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" },\n",
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" {\n",
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" \"name\": \"Sqft\",\n",
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" \"type\": \"float\"\n",
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" }\n",
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" \n",
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" ],\n",
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" \"transformers\":[\n",
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" {\n",
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" \"name\": \"log(Sqft)\",\n",
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" \"transformer\": \"Sqft_log\"\n",
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" }\n",
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" ],\n",
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" \"output\": {\n",
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" \"name\": \"Rent\",\n",
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" \"type\": \"float\"\n",
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" }\n",
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"})"
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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": null,
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"metadata": {
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"id": "amUWG6386dyn"
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},
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"outputs": [],
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"source": [
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"annArborDf['log(Rent)'] = annArborDf.apply(lambda row: math.log(row['Rent']), axis=1)"
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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": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "LxcjPBLn6iAq",
|
|
"outputId": "f827bc12-0083-4fb9-ea95-53a58cc0999b"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"rentSqftModel4 = sm.OLS(\n",
|
|
" annArborDf[\"log(Rent)\"],\n",
|
|
" sm.add_constant(annArborDf[[\"Beds\", \"Baths\", \"Sqft\"]])\n",
|
|
")\n",
|
|
"rentSqftModel4Fit = rentSqftModel4.fit()\n",
|
|
"print(rentSqftModel4Fit.summary())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "WM5h3QnN60IY",
|
|
"outputId": "56dd02c1-b8a8-4fcc-951f-676d574e6a62"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"logRentBedsBathsLogSqftModel = sm.OLS(\n",
|
|
" annArborDf[\"log(Rent)\"],\n",
|
|
" sm.add_constant(annArborDf[[\"Beds\", \"Baths\", \"log(Sqft)\"]])\n",
|
|
")\n",
|
|
"logRentBedsBathsLogSqftModelFit = logRentBedsBathsLogSqftModel.fit()\n",
|
|
"print(logRentBedsBathsLogSqftModelFit.summary())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from functions.exportModel import exportModel\n",
|
|
"exportModel({\n",
|
|
" \"modelName\": \"logRentBedsBathsLogSqftModelFit\",\n",
|
|
" \"model\": logRentBedsBathsLogSqftModelFit,\n",
|
|
" \"description\": \"Predict log(Rent) based on Beds,Baths,log(Sqft) for annArborDf\",\n",
|
|
" \"modelType\": \"sm.OLS\",\n",
|
|
" \"baseRelativePath\": \"..\",\n",
|
|
" \"inputs\": [\n",
|
|
" {\n",
|
|
" \"name\": \"const\",\n",
|
|
" \"type\": \"int\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"Beds\",\n",
|
|
" \"type\": \"int\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"Baths\",\n",
|
|
" \"type\": \"int\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"Sqft\",\n",
|
|
" \"type\": \"float\"\n",
|
|
" }\n",
|
|
" \n",
|
|
" ],\n",
|
|
" \"transformers\":[\n",
|
|
" {\n",
|
|
" \"name\": \"log(Sqft)\",\n",
|
|
" \"transformer\": \"Sqft_log\"\n",
|
|
" }\n",
|
|
" ],\n",
|
|
" \"output\": {\n",
|
|
" \"name\": \"log(Rent)\",\n",
|
|
" \"type\": \"float\"\n",
|
|
" }\n",
|
|
"})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "1PHrUcM6694a",
|
|
"outputId": "7b463d70-25d1-4073-bf7e-4e93f31c5fb2"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"rentSqftModel6 = sm.OLS(\n",
|
|
" annArborDf[\"log(Rent)\"],\n",
|
|
" sm.add_constant(annArborDf[[\"Beds\", \"log(Sqft)\"]])\n",
|
|
")\n",
|
|
"rentSqftModel6Fit = rentSqftModel6.fit()\n",
|
|
"print(rentSqftModel6Fit.summary())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 430
|
|
},
|
|
"id": "BybWTp_k7hzc",
|
|
"outputId": "335b1499-534c-47d2-bdb6-7c0f3b456160"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# plt.scatter(\n",
|
|
"# annArborDf[\"Sqft\"],\n",
|
|
"# annArborDf[\"Rent\"],\n",
|
|
"# color='blue',\n",
|
|
"# alpha=0.9,\n",
|
|
"# label='Data Points - scatter',\n",
|
|
"# )\n",
|
|
"\n",
|
|
"plt.scatter(\n",
|
|
" annArborDf[\"log(Sqft)\"],\n",
|
|
" annArborDf[\"Rent\"],\n",
|
|
" color='red',\n",
|
|
" alpha=0.9,\n",
|
|
" label='Data Points - scatter',\n",
|
|
")\n",
|
|
"\n",
|
|
"# plt.scatter(\n",
|
|
"# annArborDf[\"log(Sqft)\"],\n",
|
|
"# annArborDf[\"log(Rent)\"],\n",
|
|
"# color='Green',\n",
|
|
"# alpha=0.9,\n",
|
|
"# label='Data Points - scatter',\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# plt.scatter(\n",
|
|
"# annArborDf[\"Sqft\"],\n",
|
|
"# annArborDf[\"log(Rent)\"],\n",
|
|
"# color='Yellow',\n",
|
|
"# alpha=0.9,\n",
|
|
"# label='Data Points - scatter',\n",
|
|
"# )\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"# plt.xlabel('Sqft')\n",
|
|
"plt.ylabel('Rent')\n",
|
|
"plt.legend()\n",
|
|
"plt.grid(True)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"provenance": []
|
|
},
|
|
"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": "3.12.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|