It contains different types of machine learning models like Linear Regression, Logistic Regression, Clustering techniques, etc
It contains the Linear Regression model and Statistical model for predicting the sales and Tuned using Ridge & Lasso (Tunning Technique for Linear Regression) and Assumptions of Linear Regression.
It contain Logistic Regression model and tuned that model by Adjusting the threshold, performed AUC Curve for decide between multiple threshold, Cross Validation like K-Fold, Stratified K-Fold.
Performed Feature Selection using ResursiveFeatureSelector, SequentialFeatureSelector. Univariate Analysis like SelectKBest, Chi-Square and Variance Threshold.
It contains Decision Tree Model and other models like Extra Trees, Random Forest, KNeighborsClassifier, SVC and Logistic Regression for Car Price Prediction. Implemented Base Decision Tree having accuracy of 97.83%, then we have Tuned the Decision Tree having accuracy of 96.02%, and lastly we have implemented Logistic regression having accuracy of 64.62%. Hence, We have conclude that the Base Decision has the Highest Accuracy of 97.83%.
It contains Random Forest Regressor and other models for predicting Taxi Fare. Prepared and analyse data. Performed feature engineering wherever applicable. Checked the distribution of key numerical variables. Trained a Random Forest model and other models with data and check it’s performance along with hyperparameter tuning.
It contains Hierarchical Clustering model for understanding of how the variables are linked to each other and will be able to apply hierarchical clustering to determine review types.
It contains Time Series model for predicting and forecasting the Sales of the Furniture.