Brief description on 15 days online Internship
DAY 1 Brief session on basics of Python Importing Libraries Emoji installation DAY 2 Decision making, Tricks, Profiling Analysis Tools,Lambda function, howdoi python module DAY 3 Panda library, Panda series Numpy library Array operations Worked with USAhousing text file. DAY 4 Linear Regression on USAhousing Gradient Descent Correlation DAY 5 Logistic Regression to obtain confusion matrix Exploratory Data Analysis DAY 6 K-Nearest Neighbors algorithm Importing libraries and dataset Splitting dataset(Social_Network_Ads.csv) into train and test Predicting the result Feature scaling Training Naive Bias model DAY 7 Training Random Forest Classification model Importing libraries and dataset (Social_Network_Ads.csv) Splitting dataset Feature Scaling Day 8 K-Means Clustering Importing libraries and dataset(Mall_Customers.csv) Used Elbow method DAY 9 Training the Hierarchical Clustering Model Used Dendogram to find optimal number of clusters Used Mall_Customers.csv dataset. DAY 10 A...