In this project i've tried to predict the prices of houses in Boston data set and deployed an end to end machine learning model using flask and heroku....
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Report Bug / Documentation / Paper -> thenomaniqbal@gmail.com
Boston housing price prediction using Regression Algorithms
- CRIM per capital crime rate by town
- ZN proportion of residential land zoned for lots over 25,000 sq.ft.
- INDUS proportion of non-retail business acres per town
- CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
- NOX nitric oxides concentration (parts per 10 million)
- RM average number of rooms per dwelling
- AGE proportion of owner-occupied units built prior to 1940
- DIS weighted distances to five Boston employment centers
- RAD index of accessibility to radial highways
10.TAX full-value property-tax rate per 10,000 USD - PTRATIO pupil-teacher ratio by town
- Black 1000(Bk — 0.63)² where Bk is the proportion of blacks by town
- LSTAT % lower status of the population
The visualization of the data set is implemented in a separate file: Visualization
thenomaniqbal, boston-housing-price-prediction, thenomaniqbal@gmail.com
Clone the repo and extract it ....
This is the list of things you need to use the software and how to install them.
- Python
Version python 3.8 <
- Pandas
- sklearn
- gunicorn
- scipy
- numpy
- matplotlib
- flask
- Clone the repo
git clone https://github.com/thenomaniqbal/boston-housing-price-prediction.git
- Install Python Libraries
pip install pandas, sklearn, numpy, matplotlib, flask, gunicorn, scipy
- Required Imports:
import numpy as np
import pandas as pd
import matplolib.pyplot as plt
from flask import Flask,request, url_for, redirect, render_template
import pickle
Noman Iqbal
Link: https://github.com/thenomaniqbal/
- Stackoverflow
- flask