100 Days of Data Science Code

Starting a 100 Days Code Challenge for Learning Data Science from Scratch is my goal on Learning Data Science in Machine Learning by:

• Learning Fundamentals of Python
• Python Libraries for Data Science
• Data Manipulation and Preprocessing
• Machine Learning Basics
• Advanced Machine Learning Techniques
• Deep Learning and Neural Networks
• Model Evaluation and Deployment
• Data Science Project and Wrap-Up

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Articles Published on LinkedIn

• Project 1: Bank Management System: Python, OOPS, and MySQL Database

• 25 days Completion: Successful Completion of 25 Days in 100 Days of Data Science Code

• 50 days Completion : Successful Completion of 50 Days in 100 Days of Data Science Code

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Calendar Progress

July 2023

Sun	Mon	Tues	Wed	Thurs	Fri	Sat
-	-	-	-	-	-	1
2	3	4	5	6	7	8
9	10	11	12	13	14	15
16	17	18 ✅	19 ✅	20 ✅	21 ✅	22 ✅
23 ✅	24 ✅	25 ✅	26 ✅	27 ✅	28 ✅	29 ✅
30 ✅	31 ✅	-	-	-	-	-

August 2023

Sun	Mon	Tues	Wed	Thurs	Fri	Sat
-	-	1 ✅	2 ✅	3 ✅	4 ✅	5 ✅
6 ✅	7 ✅	8 ✅	9 ✅	10 ✅	11 ✅	12 ✅
13 ✅	14 ✅	15 ✅	16 ✅	17 ✅	18 ✅	19 ✅
20 ✅	21 ✅	22 ✅	23 ✅	24 ✅	25 ✅	26 ✅
27 ✅	28 ✅	29 ✅	30 ✅	31 ✅	-	-

September 2023

Sun	Mon	Tues	Wed	Thurs	Fri	Sat
-	-	-	-	-	1 ✅	2 ✅
3 ✅	4 ✅	5 ✅	6 ✅	7 ✅	8 ✅	9 ✅
10 ✅	11 ✅	12 ✅	13 ✅	14 ✅	15 ✅	16 ✅
17 ✅	18 ✅	19 ✅	20 ✅	21 ✅	22 ✅	23 ✅
24 ✅	25 ✅	26 ✅	27 ✅	28 ✅	29 ✅	30 ✅

October 2023

Sun	Mon	Tues	Wed	Thurs	Fri	Sat
1 ✅	2 ✅	3	4	5	6	7
8	9	10	11	12	13	14
15	16	17	18	19	20	21
22	23	24	25	26	27	28
29	30	31	-	-	-	-

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100 Days of Data Science Code Day-to-Day Progress

DAY 1 (18 July 2023):

Goal: Python Basics

• Control flow statements like if-else conditions and loops.

Github Repository: Source Code

LinkedIn post: Daily Update

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DAY 2 (19 July 2023):

Goal: Functions and Modules

• Concept of modules.
• How to import and use built-in modules as well as create your own.

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 3 (20 July 2023):

Goal: Data Structures

• Python's built-in data structures such as lists, tuples, dictionaries, and sets.
• Also, learn about indexing, slicing, and manipulating these data structures.

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 4 (21 July 2023):

Goal: File Handling and Exception Handling

• Read from and write to files in Python.
• Learn about exception handling and how to handle errors using try-except blocks.

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 5 (22 July 2023):

Goal: Python Classes and Objects

• Class Declaration
• Object Instantiation
• Constructor and Destructor
• Built-in Class Attributes and Functions
• Instance, Class and Static Variables and Functions.

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 6 (23 July 2023):

Goal: Python OOPs Concepts and Implementation in Python

• Data Abstraction
• Encapsulation
• Inheritance
• Polymorphism.

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 7 (24 July 2023):

Goal: Advanced Python Concepts

• Higher Order Functions
• List Comprehensions
• Regular Expressions (RegEx)

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 8 (25 July 2023):

Goal: Python Connectivity with MySQL Database

• Setting Up MySQL Connection
• Executing SQL Queries.

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 9 (26 July 2023):

Goal: Day 1 of Bank Management System

• Database Setup
• Python Environment Setup
• Database Connectivity
• Create Basic Classes
• Customer Management.

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 10 (27 July 2023):

Goal: Day 2 of Bank Management System

• Account Management(Create Account, List Account Details)
• Basic Error Handling(Apply Validations on Input values)
• Testing and Debugging(Checking Input value validations).

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 11 (28 July 2023):

Goal: Final Day of Project (Transfer Operations and Final Testing)

• Transfer Operation
• Final Testing and Documentation
• Clean Up and Deployment.

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 12 (29 July 2023):

Goal: NumPy Basics and Array Manipulation

• Introduction to NumPy
• Installing NumPy
• Creating NumPy arrays
• Array indexing and slicing
• Array reshaping and resizing
• Stacking and splitting arrays.

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 13 (30 July 2023):

Goal: Mathematical Operations with NumPy

• Element-wise Operations
• Aggregation Functions
• Linear Algebra with NumPy.

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 14 (31 July 2023):

Goal: Statistics Functions with NumPy

• Descriptive statistics
• Random number generation
• Sorting and searching arrays

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 15 (1 Aug. 2023):

Goal: Introduction to Pandas and Data Structures in Pandas

• Introduction to Pandas
• Install Pandas
• Types of Data Structures : Series, DataFrames
• Importing and Exporting DataFrames
• DataFrame Functions
• Accessing DataFrames : Indexing, Slicing, loc[], iloc[].

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 16 (2 Aug. 2023):

Goal: Data Manipulation and Data Aggregation using Pandas

• Advanced Indexing and Selection - (Label-based indexing, boolean indexing, and advanced slicing)
• Combining DataFrames - (Concatenation, merging, and joining techniques)
• Data Manipulation
• Advanced Data Manipulation - (reshaping data, pivoting, and melting)
• Data Aggregation and Grouping - (groupby() and other aggregation Functions)

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 17 (3 Aug. 2023):

Goal: Data Cleaning

• Basic Data Cleaning and Pre-Processing:
  • Removing Duplicates
  • Fixing Wrong Data
  • Cleaning Data of Wrong Format
  • Cleaning Empty Cells
  • dropna(), fillna()
  • drop_duplicates()
• Data Transformation - ( apply() and map() )
• Working with Text Data - Functions of str attribute

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 18 (4 Aug. 2023):

Goal: Feature Engineering and Time Series Analysis

• Feature Engineering:
  • Data Normalization
  • Data Scaling
  • Data Standardization
• Time Series Analysis and Resampling:
  • Working with datetime data
  • Date offsets
  • Resampling time series data
  • Datetime index

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 19 (5 Aug. 2023):

Goal: Matplotlib Introduction and Line Plots

• Matplotlib:
  • Installation of Matplotlib library
  • Import Matplotlib library
• Matplotlib Pyplot:
  • Plotting x and y points
  • Plotting without line
  • Matplotlib Markers (Types, Color, Size)
  • Matplotlib Line (LineStyle, Line colors, line width)
  • Single Plot with multiple lines
  • Matplotlib Labels and Title (Create Label, Create Title, Set font properties to Title and Label, Title Position)
  • Adding Grid Lines (Line Properties of grid)
• Matplotlib Bars:
  • Vertical Bars
  • Horizontal Bars
  • Bar colors
  • Bar width
  • Bar height

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 20 (6 Aug. 2023):

Goal: Matplotlib Scatter Plot and Histogram

• Subplots:
  • subplot() function
  • Title for each subplot
  • Super title of Plot
• Matplotlib Scatter Plot:
  • Create Scatter Plots
  • Compare Plots
  • Color each dots
  • ColorMap for dots
  • Combine Color, Size and Alpha values
• Matplotlib Histograms:
  • Create Histogram
• Matplotlib Pie Charts:
  • Create Pie Chart
  • Labels
  • startAngle
  • Explode
  • Shadow
  • Colors
  • Legend
  • Header

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 21 (7 Aug. 2023):

Goal: Seaborn Introduction

• Seaborn:
  • Installation of Seaborn
  • Import Seaborn library
• Different types of plots:
  • Relational Plots
  • Categorical Plots
  • Distribution Plots
  • Regression Plots
• Categorical Plots:
  • Bar Plot
  • Count Plot
  • Box Plot
  • Violinplot
  • Stripplot
  • Swarmplot
  • Factorplot
• Distribution Plots:
  • Histogram
  • Distplot
  • Jointplot
  • Pairplot
  • Rugplot
  • KDE Plot

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 22 (8 Aug. 2023):

Goal: Seaborn Visualization Plots - Relational and Regression Plots

• Customizing Seaborn Plots:

  • Changing Figure Asthetics
  • Removal of Spines
  • Changing the Figure size
  • Scaling the plots
  • Setting the Style Temporarily
  • Color Palette - (Diverging, Sequential, Default color palette)

• Multiple Plots with Seaborn:

  • Using Matplotlib - (add_axes(), subplot(), subplot2grid() functions)
  • Using Seaborn - (FacetGrid() method, PairGrid() method)

• Relational Plot Types:

  • relplot()
  • Scatter Plot
  • Line Plot

• Regression Plot Types:

  • lmplot
  • RegPlot

• Matrix Plots:

  • HeatMap
  • Clustermap

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 23 (9 Aug. 2023):

Goal: Python Fundamentals Notes

• Introduction
  • Features
  • Applications
• Identifiers:
  • Keywords
  • Variables and Constants
• Operators in python
• Data types in python
  • String data type and operations
  • List data type and operations
  • Tuple data type and operations
  • Set data type and operations
  • Dictionary data type and operations
• Control Statements in python:
  • Decision making
  • looping statements
  • looping control statements

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 24 (10 Aug. 2023):

Goal: Python Fundamentals Notes

• Introduction
  • Installation
  • Import
• Create arrays in python
• Array creation using NumPy Functions
  • zeros
  • ones
  • arange
  • linspace
  • eye
  • identity
  • fromiter
• Accessing array elements
  • Indexing and Slicing
• Random number Generation
  • rand()
  • random()
  • ranf()
  • randint()
  • randn()

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 25 (11 Aug 2023):

Goal: Pandas Revision

• Introduction - Install, Import
• Data Structures:
  • Series
  • DataFrames
• DataFrames
  • Importing and Exporting
  • Functions - columns, describe(), info(), head(), tail(), isna()
  • Accessing DataFrames - loc[], iloc[],
• Basic Data Cleaning:
  • Empty Cells
  • Wrong Format Data
  • Fixing Wrong Data
  • Removing Duplicates
• Apply filters
  • apply()
  • map() - Using Dictionary, Series, Function for mapping

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 26 (12 Aug 2023):

Goal: Introduction to Artificial Intelligence and Machine Learning Fundamentals

• Artificial Intelligence:
• Machine Learning:
  • Difference between Artificial Intelligence and Machine Learning
  • Applications of Machine Learning
  • Limitations of Machine Learning
  • Types of Machine Learning
    • Supervised Learning
    • Unsepervised Learning
    • Reinforcement Learning
  • Comparisons between all types

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 27 (13 Aug 2023):

Goal: Understanding Machine Learning Workflow

• 1. Data Preprocessing:
  • Data Cleaning
  • Feature Selection/Extraction
  • Normalization/Scaling
  • Encoding Categorical Variables
  • Splitting Data
• 2. Model Training:
  • Selecting a Model
  • Initializing Parameters
  • Training Loop
  • Gradient Descent (for Optimization)
  • Hyperparameter Tuning
• 3. Model Evaluation:
  • Metrics
  • Cross-Validation
  • Confusion Matrix
  • ROC and AUC
  • Overfitting and Underfitting

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 28 (14 Aug 2023):

Goal: Model Evaluation Techniques in Machine Learning

• Cross-Validation
• Evaluation Metrics:
  • Accuracy
  • Precision
  • Recall
  • F1-Score
  • Area Under Curve (AUC) and Receiver Operating Characteristic (ROC)
• Confusion Matrix
• Overfitting and Underfitting Detection:
  • Overfitting
  • Underfitting

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 29 (15 Aug 2023):

Goal: Diagnosing and Addressing Underfitting and Overfitting

• Underfitting:
  • Choosing a more complex model
  • Adding more features
  • Fine-tuning hyperparameters
• Overfitting:
  • Collect more data
  • Feature selection
  • Cross-validation
  • Regularization techniques
  • Early stopping

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 30 (16 Aug 2023):

Goal: Simple Linear Regression Implementation

• Linear Regression Introduction
• Simple Linear Regression:
  • Assumptions of Simple LR
  • Equation of Simple LR
  • Applications of Linear Regression
  • Working of Linear Regression
  • Finding goodness of fit
  • Examples of Linear Regression
  • Implementation of Simple Linear Regression
  • Real-world Application: Salary Prediction

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 31 (17 Aug 2023):

Goal: Multiple Linear Regression and Implementation using Student Performance Analysis

• Multiple Linear Regression (MLR):
  • Key points of MLR
  • Equation of MLR
  • Assumptions of MLR
  • Implementation of MLR using Python
  • Real-world Application: Student Performance Analysis

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 32 (18 Aug 2023):

Goal: Classification in Machine Learning

• Classification

• Types of Learners:

  • Lazy Learners: Firstly, store dataset and wait until receive test dataset.
  • Eager Learner: Develop classification model based on training dataset, before receiving testing dataset.

• Types of Classification Algorithms:

  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Neural Networks

• Terminologies in Classification:

  • Features and Labels
  • Training and Testing Data
  • Confusion Matrix
  • Precision, Recall, F1-Score
  • ROC and AUC Curve

• Types of Classification:

  • Binary Classification: Two classes (e.g., Yes/No)
  • Multiclass Classification: Multiple distinct classes (e.g., Cat/Dog/Horse)

• Models' Evaluation Techniques for Classification: Used for finding goodness of model's fit:

  • Accuracy
  • Precision and Recall
  • F1-Score
  • ROC Curve and AUC
  • Confusion Matrix

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 33 (19 Aug 2023):

Goal: Logistic Regression Implementation

• Logistic Regression:
  • Logistic Function (Sigmoid Function)
  • Assumptions of Logistic Regression
  • Types of Logistic Regression:
    • Binary / Binomial
    • Multinomial
    • Ordinal
  • Terminologies involved in Logistic Regression
  • Implementation of Logistic Regression
• Difference between Linear Regression and Logistic Regression

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 34 (20 Aug 2023):

Goal: Decision Tree Concepts

• Decision Tree:
  • Components of a Decision Tree
    • Root Node
    • Internal Nodes
    • Leaf Nodes
  • Attribute Selection Measures(ASM):
    • Entropy
    • Information Gain
    • Gini Index
  • How Decision Trees Work
  • Advantages of Decision Trees

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 35 (21 Aug 2023):

Goal: Decision Tree Implementation

• Decision Tree Implementation Setup:
  • Data Pre-processing
  • Model Training
  • Predicting the Results
  • Model Evaluation Techniques
• Examples for Decision Tree Implementation:
  • IRIS Flower Classification
  • Red Wine Quality Prediction

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 36 (22 Aug 2023):

Goal: Ensemble Methods

• Ensemble Methods:
  • Bagging
  • Boosting
  • Stacking
  • Advantages of Ensemble Methods

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 37 (23 Aug 2023):

Goal: Gradient Boosting in Machine Learning

• Gradient Boosting in Machine Learning:
  • What is Gradient Boosting
  • Key Components of Gradient Boosting
  • How Gradient Boosting Works
  • Benefits of Gradient Boosting

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 38 (24 Aug 2023):

Goal: AdaBoost and XGBoost

• AdaBoost and XGBoost:
  • AdaBoost (Adaptive Boosting)
  • XGBoost (Extreme Gradient Boosting)
  • Advantages of AdaBoost and XGBoost
  • Applications

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 39 (25 Aug 2023):

Goal: Random Forests Introduction

• Random Forests:
  • What are Random Forests
  • Key Components of Random Forests
  • How Random Forests Work
  • Benefits of Random Forests
  • Real-world Applications of Random Forests

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 40 (26 Aug 2023):

Goal: Random Forest Implementation and Hyperparameter Tuning

• Random Forest Implementation:
  • Step-by-Step Approach
  • IRIS Flower Prediction
  • Red Wine Quality Prediction
• Hyperparameter Tuning:
  • Unlocking Model Potential
  • GridSearchCV
  • RandomizedSearchCV

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 41 (27 Aug 2023):

Goal: Decision Tree and Random Forest Example

• Decision Tree in Action
• Enchantment of Random Forests
• Social Media Ads prediction

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 42 (28 Aug 2023):

Goal: Support Vector Machine (SVM) Introduction

• Introduction to SVM
• Terminologies used in SVM
• Advantages of SVM
• Limitations of SVM

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 43 (29 Aug 2023):

Goal: SVM Implementation

• SVM Implementation:
  • Linear SVM (Social Media Ads) : Kaggle Notebook
  • Non-Linear SVM (IRIS Flower Prediction) : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 44 (30 Aug 2023):

Goal: SVM Regression Implementation

• SVM Regression Implementation:
  • Salary Prediction : Kaggle Notebook
  • Boston Housing Price Prediction : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 45 (31 Aug 2023):

Goal: Introduction to KNN

• KNN Introduction
• Distance Metrics:
  • Euclidean Distance
  • Manhatten Distance
  • Minkowski Distance
• How KNN works
• How to choose value of 'K'

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 46 (1 Sept 2023):

Goal: KNN Implementation

• KNN Classification:
  • IRIS Flower Prediction : Kaggle Notebook
  • Mushroom Clasification : Kaggle Notebook

• KNN Regression:
  • Employee Salary Prediciton : Kaggle Notebook
  • Student Performance Prediction : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 47 (2 Sept 2023):

Goal: KNN Hyperparameter Tuning

• KNN Regression:
  • House Price Prediction : Kaggle Notebook
• KNN Classification:
  • BMI Classification : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 48 (3 Sept 2023):

Goal: ML Fundamentals Revision

• What is AI
• What is ML
• Machine Learning
• Model Evaluation Techniques in ML
  • Classification: Accuracy Score, Confusion Matrix, Classification Report
  • Regression: Mean Absolute Errors,Mean Square Errors, Root Mean Square Errors
• Exploratory Data Analysis (EDA)
• Handling Outliers
  • Removing Outliers
  • Transforming Values

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 49 (4 Sept 2023):

Goal: 5G Resource Allocation Capstone Project - MLR, SVR and KNN Regression Models

• Resource Allocation in 5G Network Service Project:
  • Data Pre-Processing
  • Implementation:
    • Polynomial Regression
    • SVM Regression
    • KNN Regression
  • Model Evaluation:
    1. Mean Absolute Errors
    2. Mean Square Errors
    3. Root Mean Square Errors
  • Kaggle Notebook : Link to Notebook
  • Comparison of Model Performances (Multiple Bar Charts)

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 50 (5 Sept 2023):

Goal: Capstone Project - Gender Classification - LR, DT, RF, SVM and KNN

• Gender Classification Project:
  • Data Pre-Processing
  • Implementation:
    • Logistic Regression
    • Decision Tree
    • Random Forest
    • SVM Classification
    • KNN Classification
  • Model Evaluation:
    1. Accuracy Score
    2. Confusion Matrix
    3. Classification Report
  • Kaggle Notebook : Link to Notebook
  • Comparison of Model Performances (Bar Chart)

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 51 (6 Sept 2023):

Goal: Introduction to Cross-Validation

• Introduction to Cross-Validation
• What is Cross Validation
• Why is Cross Validation Important
• Advantages of Cross Validation
• Limitations of Cross Validation
• Types of Cross-Validation:
  • Leave-One-Out Cross-Validation (LOOCV)
  • Leave-P-Out Cross Validation (LPOCV)
  • K-Fold Cross-Validation
  • Stratified K-Fold Cross-Validation

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 52 (7 Sept 2023):

Goal: Cross-Validation Implementation

• K-Fold Cross-Validation : Kaggle Notebook

• Stratified K-Fold Cross-Validation : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 53 (8 Sept 2023):

Goal: Perform EDA Operation on Different Datasets

• EDA on Walmart Dataset : Kaggle Notebook

• EDA on Fifa 19 Dataset : Kaggle Notebook

• EDA on Restaurant Dataset : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 54 (9 Sept 2023):

Goal: Introduction to Dimensionality Reduction

• The Curse of Dimensionality
• The Importance of Dimensionality Reduction
• Dimensionality Reduction Techniques:
  • Feature Selection
  • Feature Extraction
  • Dimension Reduction

• Advantages of Dimensionality Reduction
• Limitations of Dimensionality Reduction

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 55 (10 Sept 2023):

Goal: Introduction to Principal Component Analysis (PCA)

• Some common terms used in PCA algorithm
• Uses of PCA
• Advantages of Principal Component Analysis
• Limitations of Principal Component Analysis

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 56 (11 Sept 2023):

Goal: Steps in PCA (Principal Component Analysis)

• Step 1 : Covariance Matrix Computation
• Step 2 : Compute Eigenvalues and Eigenvectors of Covariance Matrix to Identify Principal Components

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 57 (12 Sept 2023):

Goal: Solve Example of PCA

• Pre-processed Data
• Calculated Covariance Matrix
• Eigenvalues and Eigenvectors
• Sorted Eigenvalues
• Select Principal Components

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 58 (13 Sept 2023):

Goal: PCA Implementation using Scikit-Learn

• Data Preparation
• Importing Scikit-learn
• Standardization
• PCA Implementation
• Explained Variance
• Dimensionality Reduction
• Visualization
• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 59 (14 Sept 2023):

Goal: Introduction to Feature Selection

• What is Feature Selection?
• Why is Feature Selection Necessary?
• Techniques in Feature Selection
  • Univariate feature selection
  • Feature importance from tree-based models
  • Recursive Feature Elimination (RFE)
  • L1-based feature selection
  • Correlation-based feature selection
• Steps in Feature Selection:
  • Data Pre-Processing
  • Feature Scoring
  • Feature Selection
• Advantages of Feature Selection:
  • Improved model performance
  • Faster training and prediction
  • Enhanced model interpretability
  • Reduced risk of overfitting
  • Easier visualization of data
• Limitations of Feature Selection:
  • It may result in information loss.
  • It can be challenging to decide which features to select.
  • Some methods might not work well for all types of data.

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 60 (15 Sept 2023):

Goal: Feature Selection : Filter Methods

• Introduction to Filter Methods

• Steps in Filter Methods:

  1. Data Pre-Processing
  2. Feature Scoring
  3. Feature Selection

• Common Techniques in Filter Methods:

  1. Correlation-based Feature Selection
  2. Information Gain
  3. Chi-square Test
  4. Fisher's Score
  5. Missing Value Ratio

• Advantages of Filter Methods:

  1. Simplicity
  2. Speed
  3. Independence

• Limitations of Filter Methods:

  1. Independence
  2. Suboptimal Results

• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 61 (16 Sept 2023):

Goal: Feature Selection : Wrapper Methods

• Introduction to Wrapper Methods

• Steps in Wrapper Methods:

  1. Subset Selection
  2. Model Building
  3. Model Evaluation

• Common Techniques in Wrapper Methods:

  1. Forward Selection Method
  2. Backward Elimination Method
  3. Exhaustive Feature Selection Method
  4. Recursive Feature Selection Method

• Advantages of Wrapper Methods:

  1. Optimal Features
  2. Model-Specific

• Limitations of Wrapper Methods:

  1. Computationally Intensive
  2. Model Dependency

• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 62 (17 Sept 2023):

Goal: Feature Selection : Wrapper Methods

• Introduction to Embedded Methods

• Steps in Embedded Methods:

  1. Feature Selection While Building
  2. Model Training
  3. Feature Importance Assessment

• Common Techniques in Embedded Methods:

  1. Random Forest Importance
  2. Lasso (L1 Regularization)
  3. Ridge (L2 Regularization)
  4. Elastic Net (L1 and L2 Regularization)

• Advantages of Embedded Methods:

  1. Feature Relevance
  2. Model Compatibility

• Limitations of Embedded Methods:

  1. Model Dependency
  2. May Miss Correlations

• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 63 (18 Sept 2023):

Goal: Exploratory Data Analysis (EDA) on IPL All Time Best Batsman Trending Dataset

• Key EDA Operations Performed:

  1. Data Loading
  2. Data Exploration
  3. Data Visualization
  4. Statistical Insights

• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 64 (19 Sept 2023):

Goal: Support Vector Regression (SVR) on Used Car Price Prediction

• Key SVR Operations Performed:

  1. Data Loading
  2. Data Pre-processing
  3. Feature Selection
  4. Splitting Data
  5. SVR Model Building
  6. Model Training
  7. Model Evaluation

• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 65 (20 Sept 2023):

Goal: Movie Recommendations Using Collaborative Filtering

• Key Operations Performed:

  1. Data Loading
  2. Data Pre-processing
  3. Collaborative Filtering
  4. Movie Recommendations

• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 66 (21 Sept 2023):

Goal: Simple Linear Regression for Insurance Predictions

• Key Operations Performed:

  1. Data Loading
  2. Data Exploration
  3. Linear Regression Implementation
  4. Model Evaluation

• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 67 (22 Sept 2023):

Goal: Simple Linear Regression for Salary Predictions

• Key Operations Performed:

  1. Data Loading
  2. Data Exploration
  3. Linear Regression Implementation
  4. Model Evaluation

• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 68 (23 Sept 2023):

Goal: Exploratory Data Analysis (EDA) for Gym Exercises Data

• Key Operations Performed:

  1. Data Loading
  2. Data Exploration
  3. Data Visualization
  4. Insights Extraction

• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 69 (24 Sept 2023):

Goal: Exploratory Data Analysis (EDA) for Life Expectancy Data

• Key Operations Performed:

  1. Data Loading
  2. Data Exploration
  3. Data Visualization
  4. Insights Extraction

• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 70 (25 Sept 2023):

Goal: Exploratory Data Analysis (EDA) on Predicting Student Dropouts

• Key Operations Performed:

  1. Data Loading
  2. Data Exploration
  3. Data Visualization
  4. Insights Extraction

• Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 71 (26 Sept 2023):

Goal: Introduction to Clustering in ML

• Intro to Clustering
• Types of Clustering:
  1. Partitioning Clustering
  2. Density-Based Clustering
  3. Distribution Model-Based Clustering
  4. Hierarchical Clustering
  5. Fuzzy Clustering

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 72 (27 Sept 2023):

Goal: Clustering Algorithms in Machine Learning

• Commonly Used Clustering Algorithms:
  • K-means Algorithm
  • Hierarchical Clustering
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
  • Agglomerative Clustering
  • Gaussian Mixture Model (GMM)
• Applications of Clustering:
  • Customer Segmentation
  • Image Compression
  • Anomaly Detection
  • Document Classification
• Advantages of Clustering:
  • Pattern Discovery
  • Data Reduction
  • Scalability
  • Interpretability

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 73 (28 Sept 2023):

Goal: Implementing K-means Clustering

• K-means Clustering:
  • Initialization
  • Assignment
  • Update Centroids
  • Repeat
• Customer Clustering : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 74 (29 Sept 2023):

Goal: K-means Clustering Implementation

• K-means Clustering:
  • Initialization
  • Assignment
  • Update Centroids
  • Repeat
• Credit Card Clustering : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 75 (30 Sept 2023):

Goal: Visualizing Clusters Distribution for 30 Datasets

• Visualize Clustering Exercises : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 76 (1 Oct 2023):

Goal: Hierarchical Clustering Implementation

• Online Retail Clustering : Kaggle Notebook

GitHub Repository: Source Code

LinkedIn post: Daily Update

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DAY 77 (2 Oct 2023):

Goal: Hierarchical Clustering Concepts

• What Can We Achieve with Hierarchical Clustering:
  • Hierarchical Insights
  • Data Exploration
  • Decision Support

GitHub Repository: Source Code

LinkedIn post: Daily Update

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