Day 16 - ML Classification Project

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21 DAYS LEARNING PLAN

Day 1 : Introduction, Data collection
Day 2 : Pandas Library
Day 3 : Numpy and data concepts
Day 4 : Matplotlib, seaborn
Day 5 : Project 1 - Exploratory Data Analysis on Sales Dataset
Day 6 : Project 2 - World Population Data Analysis
Day 7 : Project 3 - Powerbi project - Population dataset
Day 8 : Statistics 1 - Sampling, Randomization, Frequency histogram and distribution, time series, bar and pie graphs, variance, correlation
Day 9 : Statistics 2 - Frequency table and stem and leaf, central tendency, variation measure, percentile and box - whisker plot, scatter diagram
Day 10 : Statistics 3 - Linear correlation, normal distribution , empirical rule, z-score and probabilities, central limit theorem
Day 11 : Machine learning for data science - preprocessing (scaling, encoding)
Day 12 : ML - outlier detection & handling, null handling
Day 13 : ML - feature selection, feature extraction, train_test_split
Day 14 : ML Algorithms
Day 15 : ML Hyperparameter tuning, Evaluation
Day 16 : project 4- Customer churn prediction using Data Science
Day 17 : project 5 - Supply Chain Optimization for a FMCG Company using DS
Day 18 : project 6 - Music dataset clustering
Day 19 : Time series introduction
Day 20 : project 7 - CO2 Emission prediction using Time Series prediction
Day 21 : project 8 - Time series analysis on Microsoft stock data