Recommending Products Or Services to the Account (Companies). 

Applied Recommendation Machine Learning Algorithms to predict the Products which company have more chances to buy in future. 

Algorithms used in Recommendation System:- KNN, Collaborative Filtering, Popularity Based Recommendation System. 

Also handled the Cold Start problem. 

Pulling data from Azure SQL database. 

Preprocessing on Data. 

Filtered last 36 months data and predict the next 6months attritions using Moving Median method and Time Series Model. 

Automate the whole process on Azure App Service. 

Analyzing the Review and predict the sentiments. 

Used Vader Algorithms to predict the 3 types of Sentiments with their percentage value and overall sentence sentiment. 

Created corpus of word for Multiple emotions and used that corpus to predict the Emotions w.r.t. Sentence using Almo-Bert model. 

Image Processing and Classification Model using CNN. 

Collection of Damage(Scratch Or Dent) car datasets from different different resources. 

Labeled the data with Scratch and Dent. 

Re-labeled the dataset with which parts is having scratch or dent. 

Train the CNN model using that dataset. 

Predicting the car has Scratch or Dent and which part is having Scratch/Dent.

In Process- Predict how much cost it will take to repair that part.