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DS-U4-Deatiled Draft

Detailed Draft of Unit 4:

Introduction to Predictive Data Analytics

  • Definition: Predictive analytics is the process of using historical data to make predictions about future events. It involves statistical algorithms and machine learning techniques to identify patterns and trends.
  • Importance: Predictive analytics is crucial in various fields such as finance, healthcare, marketing, and more. It helps in decision-making, risk management, and strategic planning.

Essential Python Libraries

  • NumPy: A fundamental package for numerical computing in Python, providing support for arrays, matrices, and a large collection of mathematical functions.
  • Pandas: A powerful data manipulation and analysis library that provides data structures like DataFrame, which makes data cleaning and preparation more efficient.
  • Matplotlib and Seaborn: Libraries for data visualization. Matplotlib provides a low-level plotting interface, while Seaborn builds on Matplotlib to provide a high-level interface for drawing attractive and informative statistical graphics.
  • Scikit-learn: A machine learning library in Python that provides simple and efficient tools for data mining and data analysis, built on NumPy, SciPy, and Matplotlib.

Basic Examples

  • Reading Data: Using Pandas to read data from CSV files.

    import pandas as pd
data = pd.read_csv('data.csv')

  • Data Operations: Filtering, sorting, and grouping data using Pandas.

    # Filtering data
filtered_data = data[data['column'] > value]

# Sorting data
sorted_data = data.sort_values(by='column')

# Grouping data
grouped_data = data.groupby('column').sum()

Data Preprocessing

  • Removing Duplicates: Identifying and removing duplicate rows in the dataset.

    data = data.drop_duplicates()

  • Transformation Using Functions or Mapping: Applying functions to transform data.

    data['column'] = data['column'].apply(lambda x: x * 2)

  • Replacing Values: Substituting specific values in the dataset.

    data['column'] = data['column'].replace({'old_value': 'new_value'})

  • Handling Missing Values: Techniques to handle missing data, such as filling or dropping them.

    # Filling missing values
data['column'] = data['column'].fillna(value)

# Dropping rows with missing values
data = data.dropna()

Types of Data Analytics

  • Predictive Analytics: Focuses on predicting future outcomes using historical data. Techniques include regression, classification, and time series analysis.
  • Descriptive Analytics: Summarizes past data to understand what has happened. Techniques include data aggregation and data mining.
  • Prescriptive Analytics: Recommends actions based on predictive analytics. Techniques include optimization and simulation.

Key Algorithms

  • Association Rule Learning:
    • Apriori Algorithm: Used to find frequent itemsets and generate association rules.
    • FP-Growth Algorithm: An efficient method for finding frequent itemsets without candidate generation.
  • Regression Analysis:
    • Linear Regression: Models the relationship between a dependent variable and one or more independent variables.
    • Logistic Regression: Used for binary classification problems.
  • Classification Algorithms:
    • Naive Bayes: A probabilistic classifier based on Bayes' theorem, assuming independence between predictors.
    • Decision Trees: A model that uses a tree-like graph of decisions and their possible consequences.

Introduction to Scikit-learn

  • Installation: Installing Scikit-learn using pip.

    pip install scikit-learn

  • Dataset: Using built-in datasets like the Iris dataset for practice.

    from sklearn.datasets import load_iris
data = load_iris()

  • Math Library: Utilizing libraries like NumPy for mathematical operations.

  • Filling Missing Values: Using Scikit-learn’s SimpleImputer to handle missing data.

    from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy='mean')
data_imputed = imputer.fit_transform(data)

  • Regression and Classification: Implementing algorithms using Scikit-learn’s API.

    from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)

from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier()
classifier.fit(X_train, y_train)

Conclusion

By completing this unit, students will gain the knowledge and skills required to perform predictive data analytics using Python. They will be able to preprocess data, apply various predictive modeling techniques, and use essential Python libraries effectively to build and evaluate predictive models.