This example takes data of cancer and uses KNN algorithm to predict the outcome. Solution also explains how we can find out optimal value of K which is required for predicting accuracy
Solution explains how we proceed with logistic analysis and calculate mcfaden R2..
This solution explains how LDA/KNN can be efficiently used for classification and prediction. Note: Here most of the predictor variables are categorical.
This method explains how discriminant score can be applied to calculate the binary response variable
This simple snippet shows how conjoint analysis can be used to measure the importance of feature and level
This case study explains how random forest model can be used for marketing data analysis and process of model fine-tuning
This script shows how logic regression can be used for prediction and its model evaluation methods
This analysis shows how we can compare two different sample and conclude whether they are having similar mean or not by using insurance sales data of two separate insurance..
Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set. Problem link is https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/
This examples shows how the decision tree helps us to identify important variables from the dataset.
This analysis uses a Bollywood music data and explains how clustering can be applied to separate them for further analysis
This project explains how we can understand the impact of independent variables on regresor or response variable
This analysis shows regression analysis step by step. Also explains how we can include variable transformation for better prediction.
This analysis shows the power of FCA and a process of Identification of latent factors from variables. FCA allows us to reduce number of variables through latent factors identification and then we can use for dependent variable interpretation.
This analysis tries to explain the issue we face in CART when response variable is skewed...