This repository contains the projects I did as an intern with Feynn Labs.
- Internship Role: Machine Learning Intern
- Internship Duration: October, 2022 to November, 2022
- Internship Mode: Virtual
For this solo project, titled Artificial Intelligence for identification of Indian snakes, I put forth the idea of building an android and/or web application to identify the Indian snakes by means of AI. This service, if implemented successfully, will be very important in not only identifing the creatures but also in saving humans from snakes and vice versa.
In this study task, I, along with four other members, worked on Market Segmentation (MS), and highlighted the key points theoretically and practically (using Python) that are vital to MS.
In this subsequent project, I collaborated with four fellow members to address the assigned task: conduct an analysis of the electric vehicles market in India through Market Segmentation techniques. Our objective was to devise a viable market entry strategy that revolves around targeting the Geographic, Demographic, Psychographic and Behavioral segments.
- Tesla, Audi, Volkswagen, Nissan, Skoda tops the list of EVs with the maximum number of models in the Indian automobile market.
- Maharashtra, Gujarat, Tamilnadu, Karnataka and Andhra Pradesh are among the top states with the majority of EV 2-wheelers while Assam, Chhattisgarh, Himachal Pradesh, Sikkim, J&K with the least.
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- Uttar Pradesh, Assam and Bihar are among the top states with the majority of EV 3-wheelers while the remaining states don't seem to depend on the same.
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- Maharashtra, Delhi, Karnataka, Kerala and Andhra Pradhesh are among the top states with the majority of EV 4-wheelers while the remaining states have less number of EV 4-wheelers.
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Kindly go through the complete project report to obtain more information of the Indian EV Market.
Mobiles have become an integral part of the lives of human beings. Today, these technical devices serve a multitude of purposes for example calling, video calls, texts, internet, mailing, playing games, taking pictures, shopping etc. Due to these very purposes, buyers often take many factors into consideration while purchasing a mobile such as brand, processor, memory size (internal & external), camera, battery backup among others. Among these considerations, however, one factor that generally causes confusion is the price. As such, we built a Machine Learning model using Linear Regression to predict the price of a mobile based on its features.
