Rahul Mohan's repositories
Multi-Linear-Regression-Q2-50_startups
Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model.
Random-Forests-Q1-Fraud_Check
Use Random Forest to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"
Association-Rules-2-my-movies
Prepare rules for the all the data sets 1) Try different values of support and confidence. Observe the change in number of rules for different support,confidence values 2) Change the minimum length in apriori algorithm 3) Visulize the obtained rules using different plots
Association-Rules-Q1-Books
Prepare rules for the all the data sets 1) Try different values of support and confidence. Observe the change in number of rules for different support,confidence values 2) Change the minimum length in apriori algorithm 3) Visulize the obtained rules using different plots
Clustering-Q1-Crime_data
Perform Clustering(Hierarchical, Kmeans & DBSCAN) for the crime data and identify the number of clusters formed and draw inferences.
Clustering-Q2-EastWestAirline
Perform clustering (hierarchical,K means clustering and DBSCAN) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained.
Decesion-Tree-Q1-Company_Data
Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale.
Decision-Tree-Q2-Fraud_Data
Use decision trees to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"
Forecasting-Q1-CocaCola
Forecast the CocaCola prices
Forecasting-Q2-Airlines
Forecast the Airlines Passengers data set. Prepare a document for each model explaining.
Hypothesis-Testing-4-Cutlets
A randomly selected sample of cutlets was collected from both units and measured? Analyze the data and draw inferences at 5% significance level. Please state the assumptions and tests that you carried out to check validity of the assumptions.
Linear-Regression-1-Delivery_time
Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python.
Linear-Regression-2-Salary_Data
Salary_hike -> Build a prediction model for Salary_hike. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python.
Multi-Linear-Regression-Q1-ToyotaCorolla
Consider only the below columns and prepare a prediction model for predicting Price.
Naive-Bayes-Salary-Data
Prepare a classification model using Naive Bayes for salary data
Neural-Networks-Q1-Forestfires
PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS
Neural-Networks-Q2-Gas_Turbines
The dataset contains 36733 instances of 11 sensor measures aggregated over one hour (by means of average or sum) from a gas turbine.
Principal-component-analysis
Perform Principal component analysis and perform clustering using first 3 principal component scores
Random-Forests-Q2-Company_Data
About the data: Let’s consider a Company dataset with around 10 variables and 400 records.
Recommendation-system
Problem statement. Build a recommender system by using cosine simillarties score.
Support-Vector-Machine-Q2-Salary_Data
Prepare a classification model using SVM for salary data
Support-Vectore-Machine-Q1-ForestFires
classify the Size_Categorie using SVM
Text-Mining-Q1-Elan_mosk
ONE: 1) Perform sentimental analysis on the Elon-musk tweets (Exlon-musk.csv)
Text-Mining-Q2-Text-Data
1) Extract reviews of any product from ecommerce website like amazon 2) Perform emotion mining
docs
The open-source repo for docs.github.com