assignments

bike-sharing-demand-prediction

Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes.

book-recommendation-system

During the last few decades, with the rise of Youtube, Amazon, Netflix, and many other such web services, recommender systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys. In a very general way, recommender systems are algorithms aimed at suggesting relevant items to users (items being movies to watch, text to read, products to buy, or anything else depending on industries). Recommender systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors. The main objective is to create a book recommendation system for users.

codewithsridhar

Config files for my GitHub profile.

computer-science

:mortar_board: Path to a free self-taught education in Computer Science!

credit_card_default_prediction

This project is aimed at predicting the case of customers default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. We can use the K-S chart to evaluate which customers will default on their credit card payments.

data-science

:bar_chart: Path to a free self-taught education in Data Science!

deep_learning_specialization_coursera

electric-vehicle-hackathon

greatest_number

To find the geatest of four number entered by the user

hackerrank-python

Solution of HackerRank problems by Sridhar

hackerrank_sql

house-prices-advanced-regression-techniques

innomatics-internship

interview-question-data-science-

leetcode

lgmvip-datascience

linux-basics

live-class-monitoring-system-face-emotion-recognition-

ml-roadmap-for-2022

A curated list of Machine learning videos, links, projects and datasets to help you conquer the ML landscape in 6 months

movies-recommender-system

play-store-app-analysis

An Exploratory Data Analysis (EDA) project on Google Play Store Apps and Reviews.

project_dream_premier_league

Analyzing a Dream Premire League to help to win a team

projects

score-prediction-using-supervised-ml

Linear Regression with Python Scikit Learn In this section we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. We will start with simple linear regression involving two variables. Simple Linear Regression In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables.

seoul-bike-sharing-demand-prediction

Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes.

snake_water_gun_game

Game of Sanke, gun and water made using python

tsf-grip-internship

tweet_practice

tweet_sentiment