Statistical_Tools_in_ML_Presentation.pptx

Statistical_Tools_in_ML_Presentati Statistical Tools in Machine Learning

Statistical tools in machine learning refer to mathematical techniques and models used to analyze data, identify patterns, and make predictions. These tools help in understanding data distributions, relationships, and variability, which are essential for building efficient machine learning models.
Key Aspects of Statistical Tools in Machine Learning:

Descriptive Statistics – Summarizing data using measures like mean, median, variance, and standard deviation.
Inferential Statistics – Drawing conclusions about populations using techniques like hypothesis testing and confidence intervals.
Probability Distributions – Understanding data behavior through distributions like Normal, Binomial, and Poisson.
Regression Analysis – Modeling relationships between variables using techniques like Linear Regression and Logistic Regression.
Hypothesis Testing – Verifying assumptions using t-tests, chi-square tests, and ANOVA.
Bayesian Statistics – Applying probability to update beliefs based on new evidence.
Dimensionality Reduction – Reducing feature space using techniques like PCA (Principal Component Analysis).
Time Series Analysis – Analyzing and forecasting sequential data trends using models like ARIMA.

1. Importance of Statistical Tools in Machine Learning

2. Introduction to Machine Learning & Statistics Statistical tools are mathematical techniques and methods used to analyze, interpret, and make decisions based on data . They play a crucial role in Machine Learning (ML) by helping in data processing, model building, and performance evaluation. Machine Learning (ML) relies on data-driven decision-making. Statistics helps in data analysis, prediction, and model evaluation. Statistical tools ensure models are reliable and accurate. Plays a crucial role in decision-making and performance improvement . Statistical tools enhance ML model accuracy and efficiency.

3. Key Statistical Concepts in ML Descriptive Statistics: Summarizes data characteristics Mean, Median, Mode, Variance, Std Dev. Inferential Statistics: Makes predictions from sample data. Ex: Sampling, Confidence Intervals, Hypothesis Testing. Probability Distributions: Normal, Poisson, and Binomial distributions. Correlation & Covariance: Understanding relationships between features. Bayesian Theorem: Helps in updating Probabilistic models and real-time learning in ML.

4. Regression Analysis in ML Linear Regression: Predicts continuous values using a straight-line formula. Logistic Regression: Used for binary classification problems. Regression measures relationships between dependent & independent variables. Polynomial Regression: Handles non-linear relationships . . Applications : House price prediction, customer behavior modeling.

5. Data Preprocessing Using Statistics Feature Scaling: Normalization (0-1 scaling), Standardization (Z-score). Handling Missing Data: Mean/Median imputation, Dropping missing values. Removing Outliers: Using Z-score, IQR method (box plots). Data Distribution Check: Helps in selecting the right ML model. Feature Selection: Removing irrelevant/weak features using statistics.

6. Probability Distributions in ML Normal Distribution Used in regression and neural networks for modeling continuous data distribution. Binomial Distribution Helps in binary classification tasks like spam detection and medical diagnosis. Poisson Distribution Predicts rare events like fraud detection, server failures, and customer arrivals . Exponential Distribution Models time until an event, useful in survival analysis and reliability engineering. Uniform Distribution Assumes equal probability, used in random sampling and machine learning initialization.

7. Real-World Applications of Statistics in ML Healthcare: Predicting diseases like cancer/diabetes using probability models. Finance: Risk assessment & fraud detection via statistical analysis. E-commerce: Personalized recommendations based on user purchase history. Manufacturing: Quality control & predictive maintenance. Social Media: Sentiment analysis using statistical NLP techniques.

8. Thank You