Pinned Repositories
Analyse-the-relationship-of-sales-with-market
Designed a Regression model to understand the relationship of sales with market. With the help of coefficients from this model, We are able understand the relationship of sales with independent variables . I could able to achieve MAPE of 11.23% and Adjusted R square of 73.79%.
Aventure_work
Microsoft Power Bi is used to explore this data, from modifying the data to making relationship between data table and lookup table. Apart from this Visualization is the key area in this project.
Big_Mart_sales
The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.
Credit_card_defaulter_data_analysis
Designed a classification model for default in payment of credit card holder using Logistic Regression in R to classify potential Defaulters an accuracy of 75%, nagalkarke r square of 37%, K-S statistics of 50%.
Customers_segmentation
Designed an Unsupervised learning using Clustering K-Means Algorithms on a data related to spending scores of customers based on 5+ features. This has helped the client to target the customer according to their income and spending score .
Databricks-Modernized-and-Migrated-view-validation-framework
forecasting-of-sales-for-every-month-of-year.
Developed multi variate time series (using ARIMA) models in R for forecasting of sales for every month of year. The dataset has been provided with an information of 2 features of 140+ months of year.
IPL_Bowlers_stats_analysis_using_excel
IPL bowlers data analysis using excel functions . In this project wrist vs Finger spinners as well as spinners vs Fasters data has been analysed to predict the best .
Loan_Prediction
Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form.
Migration_Validation_Testing_Framework
vyas4853's Repositories
vyas4853/Analyse-the-relationship-of-sales-with-market
Designed a Regression model to understand the relationship of sales with market. With the help of coefficients from this model, We are able understand the relationship of sales with independent variables . I could able to achieve MAPE of 11.23% and Adjusted R square of 73.79%.
vyas4853/Aventure_work
Microsoft Power Bi is used to explore this data, from modifying the data to making relationship between data table and lookup table. Apart from this Visualization is the key area in this project.
vyas4853/Big_Mart_sales
The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.
vyas4853/Credit_card_defaulter_data_analysis
Designed a classification model for default in payment of credit card holder using Logistic Regression in R to classify potential Defaulters an accuracy of 75%, nagalkarke r square of 37%, K-S statistics of 50%.
vyas4853/Customers_segmentation
Designed an Unsupervised learning using Clustering K-Means Algorithms on a data related to spending scores of customers based on 5+ features. This has helped the client to target the customer according to their income and spending score .
vyas4853/Databricks-Modernized-and-Migrated-view-validation-framework
vyas4853/forecasting-of-sales-for-every-month-of-year.
Developed multi variate time series (using ARIMA) models in R for forecasting of sales for every month of year. The dataset has been provided with an information of 2 features of 140+ months of year.
vyas4853/IPL_Bowlers_stats_analysis_using_excel
IPL bowlers data analysis using excel functions . In this project wrist vs Finger spinners as well as spinners vs Fasters data has been analysed to predict the best .
vyas4853/Loan_Prediction
Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form.
vyas4853/Migration_Validation_Testing_Framework
vyas4853/Predict-Customer-Life-time-Value-for-an-Auto-Insurance-Company
For an Auto Insurance company, predict the customer life time value (CLV). CLV is the total revenue the client will derive from their entire relationship with a customer. Because we don't know how long each customer relationship will be, we make a good estimate and state CLV as a periodic value
vyas4853/Regression-model-to-predict-sales-of-item
Designed a Regression model to predict sales of item . The dataset has been provided with an information of 8 features of 7000+ items. I could able to achieve MAPE of 81.1%, Adjusted R square of 50.02% on my test dataset of 7000+ items by satisfying all the three assumptions of residual analysis
vyas4853/Restaurant-Review-Sentiment-Analysis
Restaurant Review Sentiment Analysis associated with the process of determining whether Review (a text unit) is positive or negative
vyas4853/Sending-CSV-File-to-EMAIL-USING-SMTP-SERVER-
vyas4853/Super-Bowl-Party-Data-Mining
Whether or not you like football, the Super Bowl is a spectacle. There's a little something for everyone at your Super Bowl party. Drama in the form of blowouts, comebacks, and controversy for the sports fan. There are the ridiculously expensive ads, some hilarious, others gut-wrenching, thought-provoking, and weird. The half-time shows with the biggest musicians in the world, sometimes riding giant mechanical tigers or leaping from the roof of the stadium. It's a show, baby. And in this notebook, we're going to find out how some of the elements of this show interact with each other. After exploring and cleaning our data a little, we're going to answer questions like: What are the most extreme game outcomes? How does the game affect television viewership? How have viewership, TV ratings, and ad cost evolved over time? Who are the most prolific musicians in terms of halftime show performances
vyas4853/Telecom_company_chrun_data_analysis
Designed a classification model for a Telecom Company churn data using Logistic Regression in R to classify Churners with an accuracy of 76%, nagalkarke RSquare of 39%, K-S statistics of 53% .
vyas4853/Uber-Graphical-data-visualization-
Graphical data visulization for Uber Pickups in New York City . This is a Graphical data visualization that will lead towards using the ggplot2 library for understanding the data and for developing a scenario for understanding the customers who avail the trips .
vyas4853/Weather-History
The Weather History is the climate change data of Hungary area, between 2006 and 2016. The Weather History includes 10+ factors and approx 10000 observations . Our main job is to explore the factors which have an impact on the Temperature in Hungary.