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 

• Apriori Algorithm
• Used Market_Basket_Optimization.csv dataset
• Data processing 
• Visualization of clusters

DAY 11

• Natural Language Processing
• Importing libraries and dataset
• Cleaning data
• Training the Naive Bias Model

DAY 12

• R squared
• Accuracy Paradox
• CAP Analysis

DAY 13

• Kernel SVM model
• Training XGBoost
• K-Fold Cross Validation

DAY 14

• Summarization of previous classes
• Usecases and other algorithms

DAY 15

• Summary of previous classes
• Worked on chatbot application