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house_rent_prediction's Introduction

House Rent Estimation/Prediction

linkedin License: MIT

Motivation

To help Landlords, Tenants and Investors accurately price rental apartment to create a transparent and easy to navigate rental market, and thereby improving the efficiency of their investment returns.

Our Goal

Installation

Use the package manager pip or homebrew to install any necessary packages for your environment if you are running on local machine.

pip install geopandas

OR

brew install geopandas

Packages Used

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set();
%matplotlib inline
from shapely.geometry import Point
import geopandas as gpd
from geopandas import GeoDataFrame
import plotly.express as px
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

Models Used

Accuracy

How to Use

Download the dataset from here
Simply clone and run .ipynb file

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

house_rent_prediction's People

Contributors

royashishneu avatar

Watchers

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