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https://github.com/rakibhhridoy/appliedmachinelearninghousing-regression

Let's take the Housing dataset which contains information about different houses in Boston. This data was originally a part of UCI Machine Learning Repository and has been removed now. We can also access this data from the scikit-learn library. The objective is to predict the value of prices of the house using the given features.
https://github.com/rakibhhridoy/appliedmachinelearninghousing-regression

deep-learning housing-market housing-prices machine-learning numpy pandas python real-estate regression scikit-learn

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Let's take the Housing dataset which contains information about different houses in Boston. This data was originally a part of UCI Machine Learning Repository and has been removed now. We can also access this data from the scikit-learn library. The objective is to predict the value of prices of the house using the given features.

Awesome Lists containing this project

README 

        
# *Applied Machine Learning Housing- Regression*



![img0](https://miro.medium.com/max/566/1*Zm2Hu724W6UQCVWWQe7afg.jpeg)



>1.Understand the requirements of the business



We are enthusiastic data scientists and before starting we need to ask some fundamental questions



 1. Why does our organisation need this predictive model?



    * possibly we are a real-estate firm and interested in investing in California

    * the organisation will use this data to feed another machine learning model

    * current process is good but manual and time consuming

    * our organisation wants an edge over competition

    * we are a consulting firm in the real-estate business and this data is valuable



2. We need to understand what are we doing at the root level



    * We’ll train our model on existing data so we are doing supervised learning

    * Since we need to predict housing prices we are doing regression

    * Output depends on many parameters so we are doing multivariate-regression



>2. Acquire the dataset

Get the dataset in CSV format here and store it in a folder. We prepare a virtual environment, activate it, install the dependencies

Start Jupyter notebook and do the basic imports

```python

%matplotlib inline

import matplotlib.pyplot as plt

import numpy as np

import os

import pandas as pdhousing = pd.read_csv('./housing.csv')

housing.head(5)

```



This data has metrics such as the ```population, median income, median housing price```, and so on for each block group in California.



```python

housing.info()



RangeIndex: 20640 entries, 0 to 20639

Data columns (total 10 columns):

longitude             20640 non-null float64

latitude              20640 non-null float64

housing_median_age    20640 non-null float64

total_rooms           20640 non-null float64

total_bedrooms        20433 non-null float64

population            20640 non-null float64

households            20640 non-null float64

median_income         20640 non-null float64

median_house_value    20640 non-null float64

ocean_proximity       20640 non-null object

dtypes: float64(9), object(1)

memory usage: 1.6+ MB

```

[Source](https://medium.com/@gurupratap.matharu/end-to-end-machine-learning-project-on-predicting-housing-prices-using-regression-7ab7832840ab)



next you will find in the notebook. 



### *Get Touch With Me*

Connect- [Linkedin](https://linkedin.com/in/rakibhhridoy) 


Website- [RakibHHridoy](https://rakibhhridoy.github.io)