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How to Approach ML Problems Step by Step !!

For THEORY PART, please refer to https://github.com/sandipanpaul21/Machine-Learning-Notes-Daywise

01 Know about the Dataset

  • What Dataset is all about? Problem Objective ?
  • Number of Rows and Columns. Data Stored is in which type? Is it a Data-frame or a Dictionary?

02 Univariate Analysis

  • Uni means one, so Single Variable Analysis
  • Mainly deals with Numerical Measures used in a dataset (Total 14 Numerical Measures)
  • Measure of Central Tendency : 1. Mean, 2. Median, 3. Mode
  • Measure of Data Spread : 4. Quartile, 5. Percentile, 6. Range, 7. IQR, 8. Boxplot, 9. Variance, 10. Standard Deviation
  • Variation between Variables : 11. Covariance, 12. Correlation Coefficient (Pearson and Spearman)
  • Measure Distribution and Peakness : 13. Skewness and 14. Kurtosis

03 Bivariate Analysis

  • Bi means two, so Two Variable Analysis
  • There are majorly two types of Data Variable: Continuous & Categorical Variable
  • So 3 possible combinations for Bivariate Analysis
    1. Continuous vs Continuous : Correlation Coefficient
    2. Categorical vs Categorical : Chi Square Test
    3. Continuous vs Categorical : T Test (n < 30), Z Test (n > 30) and ANOVA Test

04 Outlier & Missing Values

  • Outlier are data points that differs significantly from other observations
  • Techniques to Detect Outliers : 1. Box Plot and 2. Z-Score
  • Technqiues to Remove Outliers : Capping Based on Upper and Lower Range
  • Missing Values in the Dataset cause concern for Machine Learning Model
  • Techniques for Imputing Missing Values
    1. Continuous Data : Median Imputation
    2. Categorical Data : Mode Imputation
    3. KNN Imputation (why better than Median and Mode imputation)

05 Feature Engineering

  • Tweaking the features, to increase the efficiency of the Model
  • 3 Major Steps in Feature Engineering : 1.Transformation, 2.Scaling & 3. Construction
  • Feature Transformation
    1. Feature transformation is performed to normalize the data
    2. Methods Used : 1.Log Transformation, 2.Square Root, 3.Cube Root & 4.Box-Cox Transformation
  • Feature Scaling
    1. Feature scaling is conducted to standardize the independent features
    2. Method Used : Mix-Max Scaler
  • Feature Construction
    1. It is a process of creating features based on the original descriptors
    2. Methods Used : 1.Binning and 2.Encoding

06 Regression Assumption

  • With Boston Dataset, all 5 regression assumptions checked (Why, What and How)
    1. Linearity between Target & Features : Plot Predicted & Target
    2. Normality of Error Term : Anderson-Darling Test & Skewness in Error Term
    3. Multicollinearity among Predictors : Correlation & VIF
    4. Autocorrelation among Error Term : Durbin-Watson Test
    5. Homoscedasticity,same variance within error terms : Residual Plot

10 K Means Clustering

  • Define number of clusters, take centroids and measure distance
  • Euclidean Distance : Measure distance between points
  • Number of Clusters defined by Elbow Method
  • Elbow Method : WCSS vs Number of Cluster
  • Silhouette Score : Goodness of Clustering

11 Hierarchical Clustering

  • Group similar objects into groups
  • Type of HC
    1. Agglomerative : Bottom Up approach
    2. Divisive : Top Down approach
  • Number of Clusters defined by Dendogram
  • Dendogram : Joining datapoints based on distance & creating clusters
  • Linkage : To calculate distance between two points of two clusters
    1. Single linkage : Minimum Distance between two clusters
    2. Complete linkage : Maximum Distance between two clusters
    3. Average linkage : Average Distance between two clusters

12 DB Scan Clustering

  • No need to give pre-define clusters
  • Distance metric is Euclidean Distance
  • Need to give 2 parameters
    1. eps : Radius of the circle
    2. min_samples : minimum data points to consider it as clusters

13 Gausian Mixture Model

  • Probablistic Model
  • Uses Expectation-Minimization (EM) steps:
    1. E Step : Probability of datapoint of each cluster
    2. M Step : For each cluster,revise parameter based on proabability