aditya5172

Bank-Loan-Prediction

Exploratory-Data-Analysis---Retail

I used Data Visualization to study the module ( which contains all the details such as profit/loss, sales, discount offered, location, etc., of all the retail shop across USA of a Superstore) to suggest where company can improve to increase the profit of the shop. With the help of Data Visualization I plotted graph for profit/loss and sales against discount offered to learn after 20% discount the company went for a loss no matter what amount of sale is made. I displayed top ten performing cities (with respect to profit). I drew graph for profit/loss and sales for each state which helped me to find the best performing state and worst performing. I also drew graph for profit/loss and sales for each product which helped me to find the most profit making product and least profit making product. Module: https://bit.ly/3i4rbWl

IPL-DATABID-Players-Auction-

Netflix-Content-Analysis

predicting-optinum-number-of-clusters

I used Machine Learning to study the module to predict the optimum number of clusters. I used elbow method to determine the optimum number of clusters with help of k-mean classification. With help of Data Visualization I plotted the clutters which helps to understand the module and group the module in a very effective way. Dataset : https://bit.ly/3kXTdox

Predicting-the-score

I used the Machine learning skill to study the module (which contained hours a student studied and marks he/she scored) to predict the score of a student with respect to the hours he studied. I used Linear Regression to determine the relation between the variables (hours a student studied and marks he/she scored) and used some data visualization to better understand the module. In order to omit the overfitting and underfitting I split the data into test and train datasets. Dataset : http://bit.ly/w-data

tvscreener

TradingView Screener API

aditya5172/IPL-DATABID-Players-Auction-

aditya5172/predicting-optinum-number-of-clusters

I used Machine Learning to study the module to predict the optimum number of clusters. I used elbow method to determine the optimum number of clusters with help of k-mean classification. With help of Data Visualization I plotted the clutters which helps to understand the module and group the module in a very effective way. Dataset : https://bit.ly/3kXTdox

aditya5172/Exploratory-Data-Analysis---Retail

I used Data Visualization to study the module ( which contains all the details such as profit/loss, sales, discount offered, location, etc., of all the retail shop across USA of a Superstore) to suggest where company can improve to increase the profit of the shop. With the help of Data Visualization I plotted graph for profit/loss and sales against discount offered to learn after 20% discount the company went for a loss no matter what amount of sale is made. I displayed top ten performing cities (with respect to profit). I drew graph for profit/loss and sales for each state which helped me to find the best performing state and worst performing. I also drew graph for profit/loss and sales for each product which helped me to find the most profit making product and least profit making product. Module: https://bit.ly/3i4rbWl

aditya5172/Bank-Loan-Prediction

aditya5172/Netflix-Content-Analysis

aditya5172/Predicting-the-score

I used the Machine learning skill to study the module (which contained hours a student studied and marks he/she scored) to predict the score of a student with respect to the hours he studied. I used Linear Regression to determine the relation between the variables (hours a student studied and marks he/she scored) and used some data visualization to better understand the module. In order to omit the overfitting and underfitting I split the data into test and train datasets. Dataset : http://bit.ly/w-data