To Prepare a classification model using SVM for salary data

Prepare support vector machines model for classifying the area under fire for forestfires data

Build a Neural Network model for 50_startups data to predict profit

Prepare a model for strength of concrete data using Neural Networks

Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data

To extract the clinical trails performed on HCC and predict the overall outcome of the trails using word cloud and sentimental analysis

Extracted the Customer reviews from IMDB on the movie “AQUAMAN” and performed wordcloud and # Sentimental analysis on the same.

Extract reviews from Amazon on Gaming Laptop and perform sentimental analysis on the same.

Extracted the Facebook User on their tweets(from twitter) and performed wordcloud and Sentimental analysis on the same

To Predict whether the salary is greater or lesser than 50K using Naive Bayes Model.

Classification of HAM\SPAM message using Naive Bayes Model

Method 2 - Prepare a model for glass classification using KNN

Prepare a model for glass classification using KNN

Implement a KNN model to classify the animals in to categories.

To Predict whether the tumor is Benign or Malignant using Random Forest

Random Forest- Predict the IRIS dataset

To Predict on Risky Vs Good Customers on Fraud Check using Random Forest

Predict the high sales of Company data using Random Forest

Predict the Stock value for MRF using Auto Arima Model

Predict the sales of Plastic for the next 10 years using Auto Arima Model

Predict the Sales for Cocacola for the next 10 years using AutoArima Model

Predict the Passengers information for the next 10 years using Auto Arima Model

Predict the Time Series Forecast of Cocacola using RMSE value

Capture the Time Series Forecast on Airline Data

To capture the association rules for groceries data and analyze their support, confidence and lift ratio.

To capture the association rules and observe their confidence, support and lift ratios.

To Observe different set of rules for movies data and capture their confidence, Support and Lift ratios

To Observe the Support ,Confidence level using apriori algorithm for Association Rules

To Prepare a model on fraud data to check on the probability of Risky Vs Good. Risky patients -Taxable Income <= 30000

To Capture the Attribute that causes high sales for the Clothing manufacturing Company

CrimeData using KMeans Clustering

East West Airlines using KMeans Clustering:

Crime Data - Clustering Using Euclidean and Complete Linkage Method.

East West Airlines - Clustering Using Euclidean and ward.D2 Linkage Method.

To Predict whether the probability or chances of having an extra marital affairs for a person based on the given Input.

Prepare a prediction model for profit of 50_startups data using multi linear regression.Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model.

This model is to predict the sales of the Computer using speed, hd, ram, screen size, cd, multi,premium,ads and trend.

Build a prediction model to predict the salary hike based on years of experience.

This model details the prediction results on how a patients weight has gained based on the calories consumed.

To predict delivery time based on sorting time

Logistic Regression Model - Bank Data. To Predict whether a client has taken a Fixed Deposit or not.

Predicting the Price of Toyota Corolla based on Age, Kilometer ran for, Horse Power, Doors, Gears, CC, Quarterly Tax and Weight.

Build a prediction model for Churn_out_rate