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mankarsnehal authored Sep 25, 2023
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Expand Up @@ -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 |
| - | - | - | - | - | - | - |

| - | - | - | - | - | - | - |



Expand Down Expand Up @@ -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/)

---



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