mandy2308's Stars
sharmaroshan/Numpy-and-Pandas
Numpy and Pandas are one of the most important building blocks of knowledge to get started in the field of Data Science, Analytics, Machine Learning, Business Intelligence, and Business Analytics. This Tutorial Focuses to help the Beginners to learn the core Concepts of Numpy and Pandas and get started with Machine Learning and Data Science.
sharmaroshan/Steel-Defect-Detection
sharmaroshan/Bitcoin-Price-Prediction
sharmaroshan/Drugs-Recommendation-using-Reviews
Analyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.
shishir349/Building-a-Movie-Recommender-Systems
Recommender System is a system that seeks to predict or filter preferences according to the user's choices. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.
shishir349/Attrition-Analysis-on-the-HR-Department
The rate of attrition or the inverse retention rate is the most commonly used metric while trying to analyze attrition. The attrition rate is typically calculated as the number of employees lost every year over the employee base. This employee base can be tricky however. Most firms just use a start of year employee count as the base. Some firms calculate it on a rolling 12 month basis to get a full year impact. This ratio becomes harder to use if your firm is growing its employee base. For example, let's say on Jan 1st of this year there were 1000 employees in the firm. Over the next 12 months we've lost 100 employees. Is it as straight forward as a 10% attrition rate. Where it gets fuzzy is how many of those 100 employees that were lost were in the seat on Jan 1st. Were all the 100 existing employees as of Jan 1st or were they new hires during the year that termed. Hence the attrition rate must be looked at in several views.
shishir349/Market-Basket-Analysis-on-Food-Items
Frequent Itemsets via Apriori Algorithm Apriori function to extract frequent itemsets for association rule mining We have a dataset of a mall with 7500 transactions of different customers buying different items from the store. We have to find correlations between the different items in the store. so that we can know if a customer is buying apple, banana and mango. what is the next item, The customer would be interested in buying from the store.
shishir349/Analyzing-the-Email-Opening-Rates
Before building an email marketing campaign, it’s important to define your goals so you know if your campaign will be a success. One of the most vital factors to consider is how many people read and engage with your emails. This is a great indicator to show if your efforts and resources are worth the investment.