Housing Price Prediction Machine Learning using Python Scikit learn | Machine Learning Project смотреть онлайн

A machine learning model that is trained on California Housing Prices dataset from the StatLib repository. We are doing supervised learning here and our aim is to do predictive analysis. During our journey we’ll understand the important tools needed to develop a powerful ML model. Our model will help us in predicting future housing prices. We’ll validate it against our test dataset.
To do an end-to-end Machine Learning project we need to do the following steps
1. Understand the requirements of the business.
2. Acquire the dataset.
3. Visualize the data to understand it better and develop our intuition.
4. Pre-process the data to make it ready to feed to our ML model.
5. Try various models and train them. Select one that we find best.
6. Fine-tune our model by tuning hyper-parameters
7. Present our solution to the team.
8. Launch, monitor, and maintain our system.
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
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
housing = pd.read_csv('./housing.csv')
housing.head(5)

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.
2. A blockgroup typically has a population of 600 to 3,000 people.
3. We will just call them “districts” for short.
housing.info()
class 'pandas.core.frame.DataFrame'
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
3. Visualize the Data
housing.hist(bins=50, figsize=(15,15))
plt.show()

Histograms
Great! We are seeing each feature of our data-set as a histogram.
I want you to take a pen and paper and write down your some comment about each one. Believe me mostly your insights would be better than mine
Done? Let’s compare
households — hmm, most districts have around 100–500 households. peak is around 4800
housing median age — well, not very bell-shaped, at 35 and 15 are two peaks. are these years? max peak is at 50. does this mean major houses in each district are more than 50 years old?!?
latitude — looks correct, at 34 and 37 degrees of latitude are major houses.
longitude — the same, at -122 and -118 degrees are major houses
median house value — hmm, this is what i need to predict. somewhat bell-shaped, at extreme right there is a surge, is y-axis dollars? does this mean most houses are above 500,000?
median income — very bell-shaped, good distribution, but is this income in dollars? There is no income above 15 so some capping has been done. most people have income between 2–5
population — most districts have population below 3000
bedrooms — hmm, we have got bedrooms for a district? looks like most districts have between 300–600 bedrooms
total rooms — again similar to the previous two. most districts have around 3000 rooms

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