Machine Learning with Python

• Explain how Python is used in data analysis and machine learning

• Write and execute Python code interactively

• Perform calculations and basic program execution

• Install and import essential Python libraries

• Work with Python environments such as Jupyter and Anaconda

• Create and manage Pandas Series and DataFrames

• Perform indexing, slicing, and filtering operations

• Import datasets from CSV, Excel, and JSON files

• Sort, rank, and analyze structured datasets

• Handle missing values and dataset inconsistencies

• Clean and preprocess raw data

• Merge and combine datasets using joins and concatenation

• Remove duplicates and correct data inconsistencies

• Transform data formats and structures

• Perform group-based aggregations

• Understand visualization structures in Python

• Create line charts, scatter plots, and histograms

• Customize axes, legends, and chart formatting

• Highlight trends and patterns visually

• Export visualizations for reporting and presentation

• Calculate mean, median, and mode

• Measure variance and standard deviation

• Analyze data distribution patterns

• Interpret spread and variability in datasets

• Use Python to summarize numerical data

• Explain null and alternative hypotheses

• Interpret Type I and Type II errors

• Perform z-tests and one-sample t-tests

• Visualize sampling distributions

• Apply chi-square and variance testing concepts

• Perform independent sample t-tests

• Conduct paired sample tests

• Evaluate equal and unequal variance assumptions

• Interpret statistical test results

• Apply hypothesis testing to real-world scenarios

• Understand the purpose of ANOVA

• Perform one-way ANOVA analysis

• Interpret group-based statistical comparisons

• Identify statistical significance across groups

• Perform two-way ANOVA analysis

• Evaluate interaction effects between variables

• Interpret complex statistical outputs

• Analyze multi-variable comparisons

• Explain what machine learning is and how it works

• Differentiate supervised and unsupervised learning

• Identify classification and regression problems

• Understand the machine learning pipeline

• Map business problems to machine learning solutions

• Build simple linear regression models

• Develop multiple linear regression models

• Evaluate model accuracy and performance

• Understand overfitting and model fit

• Interpret RMSE and goodness-of-fit metrics

• Understand binary classification problems

• Prepare datasets for classification models

• Build logistic regression models

• Evaluate models using confusion matrices

• Interpret classification accuracy and results

• Identify trends and seasonality in time series data

• Perform autocorrelation analysis using ACF and PACF

• Build AR, ARMA, and ARIMA models

• Apply automatic ARIMA forecasting

• Understand unsupervised learning concepts

•  Apply K-Means clustering techniques

• Visualize clustering results

• Evaluate clustering effectiveness

• Compare clustering approaches

• Understand decision tree algorithms

• Build decision tree classification models

• Train random forest models

• Compare tree-based modeling approach

• Evaluate model accuracy and performance

Design and implement a machine learning solution using Python, data preprocessing techniques, predictive models, and evaluation workflows on a real-world dataset.