21-DAYS-PROGRAMMING-CHALLENGE-ACES

@@ Exploring sklearn! @@

Bit intro About library

A python library built upon NumPy ,SciPy and Matplotlib orignal name scikit-learn.

Installation

pip install scikit-learn

Features of Sklearn

1. Supervised Learning Model
2. Unsupervised learning Model
3. Clustering
4. Dimenstionality Reduction
5. Ensemble Methods
6. Feature Extraction
7. Feature Selection
8. Open Source

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💠 Day 1 :Sklearn Modelling Process:

1. Loading ,splitting data
2. Training Model
3. Model Persistence
4. Preprocessing the Dataset(Binarisation,Mean Removal ,Scaling,Noemalisation(L1,L2 normalisation))

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💠 Day 2:Linear Modelling :

1. Linear Regression (SL)(Regression) ( logit or MaxEnt Classifier)

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💠 Day 3:Linear Modelling :

2. Logistic Regression (SL)(Classification)
3. Lasso
4. Ridge
5. ElasticNet

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💠 Day 4:Gradient Descent Algorithm

1. Batch Gradient Descent
2. Stochastic Gradient Descent
3. Mini Batch Gradient Descent

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💠 Day 5:Suppot Vector Machine

1. SVM (SL,Classification+Regression)

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💠 Day 6:KNN Algorithm

1. KNN as Classifier (SL,Classification+Regression)
2. KNN as Regressor

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💠 Day 7:Metrics and scoring

(Not did much read a bit theory)

1. Confusion_matrix
2. Accuracy
3. Precision
4. Recall or Sensitivity
5. Specificity

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💠 Day 8:PCA

1. Incremental PCA (UL + dimensionality Reduction)
2. Kernel PCA

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💠 Day 9:Tree

1. Decision Tree (ID3 iterative dichotomiser 3)(SL,CART)
2. Random Forest

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💠 Day 10:Naive Bais

1. Gaussian Naive Bayes (Classification)

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💠 Day 11:Dimension Reduction

1. Principal Component Analysis(PCA)

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💠 Day 12:Dimension Reduction

2. Singular Vector Decomposition(SVD) [not did much today kam hai kafi!]

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💠 Day 13:Ensemble methods

1. Voting Classifier
   Soft Voting + with GridSearchCV

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💠 Day 14:Gradient Boosting

Read theory about all

1. GBA

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💠 Day 15:DATA PROCESSING

Steps involved in data processing

1. Treating up missing values
2. Treating outliners
3. Dimentionality Reduction
4. Variable Transformation and Feature Engineering

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💠 Day 16:Recommender System

1. Simple REcommende using IBM formula

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💠 Day 17:Recommender System

1. Content based Recommendation(tfid)

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💠 Day 18:Mean shift Clustering Algorithm

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💠 Day 19: Not a good day

1. Not having laptop with me😥 signed in through phone will read about different types of regression. no code today 😔.

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💠 Day 20: Pipeline

1. How to create one and use.
   Laptop didn't come today.
   Now I am pro at using GitHub on phone.💁

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💠 Day 21 Anomaly detection

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RESOUCES

Tutorial

Happy to complete this Chanllenge and for sure will continue Learning! 😊