machine learning hackathon on Analytics Vidhya

Sales data for 1559 products across 10 stores in different cities. Certain attributes of each product and store have been defined. The goal is to build a predictive model and find out the sales of each product at a particular store. Using a model, data scientist will try to understand important factor that affects the sales of products and stores which play a key role in increasing sales.

Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

Installation: To run this notebook interactively:

Download this repository by executing this from the terminal: git clone https://github.com/dilrubapalabiyik/bigmartsales Install virtualenv. Navigate to the directory where you unzipped or cloned the repo and create a virtual environment with virtualenv env. Activate the environment with source env/bin/activate Install the required dependencies with pip install -r requirements.txt. Execute Ipython notebook from the command line or terminal. Click Bigmart_sales.ipynb on the IPython Notebook dashboard When you're done deactivate the virtual environment with deactivate. Dependencies:

• NumPy

• IPython

• Pandas

• SciKit-Learn

• SciPy

• StatsModels

• Seaborn

• Matplotlib

This Notebook will show basic examples of:

DATA HANDLING

• Importing Data with Pandas

• Cleaning Data

• Exploring Data through Visualizations with Matplotlib

DATA ANALYSIS(still on progress)

• Supervised Machine learning Techniques:

• Logit Regression Model

• Plotting results

• Linear Regression Model

• Plotting results

• Random Forest Model

• Plotting results

VALUATION OF ANALYSIS ( still on progress)

• K-folds cross validation to valuate results locally

• Bootstrap Tests

Website for the competition: https://datahack.analyticsvidhya.com/contest/practice-problem-big-mart-sales-iii/