This is a simple machine learning project which predicts the houses in the banglore city using model built with the logistic Regression Algorithm. This project aims to apply various Python tools to clean the data, to remove some outliers to make it ready to apply machine learning algorithm and prepare a model with it so that it can be used for prediction later.
Client: HTML, CSS
Server: scikit-learn, numpy, pandas, flask, json, pickle
Softwares Used: Jupyter Notebook, Pycharm Community Edition, Sublime Text 3
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Like almost every data science or machine leanring project the data that I have picked up from kaggle for this project was also very irregular, filled with null values, So the first learning I have got from this project was how the data is standardized and how to reduce the various anomalies in the dataset. I had not only to deal with null values from the dataset but also the text data was also needed convertion into numbers as a requirement of model training process. I also learned to reduce the outliers in the data
Along with implementation of the project. Deployement is also important The project is deployed on heroku using github integration facility of heroku
visit : house price prediction
Run this project at your local system using git bash at whatever location you want.
Clone the project
git clone https://github.com/vinay-alt/house_price_pred.git
Go to the project directory
cd house_price_pred
Install dependencies
pip Install -r requirements.txt
Start the server
python App.py