TASK:
Assignment 1 : Predict the price of a house Dataset: <DS - Assignment Part 1 data set.xlsx> Link: https://www.dropbox.com/sh/aypq6h3254207bs/AACzMLvo-XtK9sYAAma6FW0la?dl=0 Problem statement: The goal is to understand the relationship between house features and how these variables affect the house price. Using more than one model, predict the price of the house using the given dataset. Please compare the accuracy of the models along with the drawbacks of each technique's assumptions before recommending the final prediction model.
Assignment 2 : Product matching Attached is sample product lists from Flipkart & Amazon <DS - Assignment Part 2 data set.zip> Link: https://www.dropbox.com/sh/aypq6h3254207bs/AACzMLvo-XtK9sYAAma6FW0la?dl=0 Problem statement: Using ML/DL techniques, match similar products from the Flipkart dataset with the Amazon dataset. Once similar products are matched, display the retail price from FK and AMZ side by side. Please explore as many techniques as possible before choosing the final technique. You may either display the final result in single table format OR You may create a simple form where we input the product name and the output of prices of the product from both websites are displayed.
STEPS:
1.Loading data
2.Data Exploration Features with Null value Numerical Features Categorical Features
3.Data Cleaning
4.Data Visualization
5.Data transformation
6.Models trained: Linear Regression model Ensemble Model
Results:
We got a good improvement in accuracy with model transition (from 56.4% to 82.7%). Needs more tuning!