diff --git a/README.md b/README.md index 7f5efad..006b220 100644 --- a/README.md +++ b/README.md @@ -65,8 +65,18 @@ Starting a 100 Days Code Challenge for Learning Data Science from Scratch is my | - | - | - | - | - | [1 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/46.%20Day%2046%20-%20KNN%20Implementation) | [2 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/47.%20Day%2047%20-%20KNN%20Hyperparameter%20Tuning) | | [3 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/48.%20Day%2048%20-%20ML%20Fundamentals%20Revision) | [4 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/49.%20Day%2049%20-%20Capstone%20Project%20-%205G%20Resources%20-%20MLR%2C%20SVR%2C%20KNN_R) | [5 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/50.%20Day%2050%20-%20Capstone%20Project%20-%20Gender%20Classification%20-%20LR%2C%20DT%2C%20RF%2C%20SVM%20and%20KNN) | [6 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/51.%20Day%2051%20-%20Intro%20to%20Cross%20Validation) | [7 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/52.%20Day%2052%20-%20Cross%20Validation%20Implementation) | [8 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/53.%20Day%2053%20-%20Perform%20EDA%20Operation) | [9 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/54.%20Day%2054%20-%20Dimensionality%20Reduction%20Intro) | | [10 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/55.%20Day%2055%20-%20Intro%20to%20PCA) | [11 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/56.%20Day%2056%20-%20Step%20in%20PCA) | [12 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/57.%20Day%2057%20-%20PCA%20Solved%20Example) | [13 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/58.%20Day%2058%20-%20PCA%20Implementation) | [14 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/59.%20Day%2059%20-%20Feature%20Selection%20Intro) | [15 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/60.%20Day%2060%20-%20Feature%20Selection%20-%20Filter%20Methods) | [16 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/61.%20Day%2061%20-%20Feature%20Selection%20-%20Wrapper%20Methods) | -| [17 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/62.%20Day%2062%20-%20Feature%20Selection%20-%20Embedded%20Methods) | [18 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/63.%20Day%2063%20-%20EDA%20on%20IPL%20Dataset) | [19 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/64.%20Day%2064%20-%20Used%20Car%20Price%20Prediction%20using%20SVR) | [20 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/65.%20Day%2065%20-%20Movies%20Recommendation) | [21 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/66.%20Day%2066%20-%20SLR%20on%20Insurance%20Dataset) | [22 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/67.%20Day%2067%20-%20Linear%20Regression%20Salary%20Dataset) | [23 ✅]() | -| [24 ✅]() | 25 | 26 | 27 | 28 | 29 | 30 | +| [17 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/62.%20Day%2062%20-%20Feature%20Selection%20-%20Embedded%20Methods) | [18 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/63.%20Day%2063%20-%20EDA%20on%20IPL%20Dataset) | [19 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/64.%20Day%2064%20-%20Used%20Car%20Price%20Prediction%20using%20SVR) | [20 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/65.%20Day%2065%20-%20Movies%20Recommendation) | [21 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/66.%20Day%2066%20-%20SLR%20on%20Insurance%20Dataset) | [22 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/67.%20Day%2067%20-%20Linear%20Regression%20Salary%20Dataset) | [23 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/68.%20Day%2068%20-%20EDA%20on%20Gym%20Exercise%20Dataset) | +| [24 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/69.%20Day%2069%20-%20EDA%20on%20Life%20Expectations%20Dataset) | [25 ✅](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/70.%20Day%2070%20-%20EDA%20on%20Student%20Dropout) | 26 | 27 | 28 | 29 | 30 | + + + + +### October 2023 + +| Sun | Mon | Tues | Wed | Thurs | Fri | Sat | +| - | - | - | - | - | - | - | + +| - | - | - | - | - | - | - | @@ -1292,26 +1302,187 @@ LinkedIn post: [Daily Update](https://www.linkedin.com/feed/update/urn:li:activi ## **DAY 62 (17 Sept 2023):** ### Goal: Feature Selection : Wrapper Methods -- Introduction to Wrapper Methods -- Steps in Wrapper Methods: - 1. -- Common Techniques in Wrapper Methods: - 1. -- Advantages of Wrapper Methods: - 1. -- Limitations of Wrapper Methods: - 1. +- 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](https://www.kaggle.com/code/snehalsanjaymankar/feature-selection-embedded-methods) + +GitHub Repository: [Source Code](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/62.%20Day%2062%20-%20Feature%20Selection%20-%20Embedded%20Methods) + +LinkedIn post: [Daily Update](https://www.linkedin.com/feed/update/urn:li:activity:7109196817849868288/) + +--- + + + +## **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](https://www.kaggle.com/snehalsanjaymankar/eda-ipl-all-time-best-batsman/edit) + +GitHub Repository: [Source Code](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/63.%20Day%2063%20-%20EDA%20on%20IPL%20Dataset) + +LinkedIn post: [Daily Update](https://www.linkedin.com/feed/update/urn:li:activity:7109583646864359425/) + +--- + + + +## **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](https://www.kaggle.com/code/snehalsanjaymankar/used-car-price-prediction-svr) + +GitHub Repository: [Source Code](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/64.%20Day%2064%20-%20Used%20Car%20Price%20Prediction%20using%20SVR) + +LinkedIn post: [Daily Update](https://www.linkedin.com/feed/update/urn:li:activity:7109950878287097856/) + +--- + + + +## **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](https://www.kaggle.com/code/snehalsanjaymankar/movie-recommendation-with-gridsearch/notebook) -- [Kaggle Notebook]() +GitHub Repository: [Source Code](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/65.%20Day%2065%20-%20Movies%20Recommendation) -GitHub Repository: [Source Code]() +LinkedIn post: [Daily Update](https://www.linkedin.com/feed/update/urn:li:activity:7110294627114450944/) + +--- + + + +## **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](https://www.kaggle.com/code/snehalsanjaymankar/slr-notebook) + +GitHub Repository: [Source Code](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/66.%20Day%2066%20-%20SLR%20on%20Insurance%20Dataset) + +LinkedIn post: [Daily Update](https://www.linkedin.com/feed/update/urn:li:activity:7110679392904736770/) + +--- -LinkedIn post: [Daily Update]() + + +## **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](https://www.kaggle.com/code/snehalsanjaymankar/salary-dataset-of-busssiness) + +GitHub Repository: [Source Code](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/67.%20Day%2067%20-%20Linear%20Regression%20Salary%20Dataset) + +LinkedIn post: [Daily Update](https://www.linkedin.com/feed/update/urn:li:activity:7111404267843796992/) --- +## **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](https://www.kaggle.com/code/snehalsanjaymankar/gym-exercises-eda) + +GitHub Repository: [Source Code](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/68.%20Day%2068%20-%20EDA%20on%20Gym%20Exercise%20Dataset) + +LinkedIn post: [Daily Update](https://www.linkedin.com/feed/update/urn:li:activity:7111627301544456192/) + +--- + + + +## **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](https://www.kaggle.com/code/snehalsanjaymankar/life-expectancy-eda) + +GitHub Repository: [Source Code](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/69.%20Day%2069%20-%20EDA%20on%20Life%20Expectations%20Dataset) + +LinkedIn post: [Daily Update](https://www.linkedin.com/feed/update/urn:li:activity:7111733521060130817/) + +--- + + + +## **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](https://www.kaggle.com/code/snehalsanjaymankar/predict-student-s-drop) + +GitHub Repository: [Source Code](https://github.com/mankarsnehal/100-Days-of-Code-Data-Science/tree/main/70.%20Day%2070%20-%20EDA%20on%20Student%20Dropout) + +LinkedIn post: [Daily Update](https://www.linkedin.com/feed/update/urn:li:activity:7112120956935925760/) + +---