Condensed Notes: Predictive Data Analytics with Python
1. Introduction
- Predictive Analytics: Uses historical data to predict future events.
- Importance: Decision-making, risk management, strategic planning.
2. Essential Python Libraries
- NumPy: Numerical operations, array/matrix support.
- Example:
import numpy as np
- Pandas: Data manipulation, DataFrames.
- Example:
import pandas as pd
- Matplotlib: Data visualization (low-level).
- Example:
import matplotlib.pyplot as plt
- Seaborn: Statistical graphics (high-level).
- Example:
import seaborn as sns
- Scikit-learn: Machine learning, data preprocessing, model evaluation.
- Example:
from sklearn.linear_model import LogisticRegression
3. Data Preprocessing
- Removing Duplicates: Ensures unique data points.
- Example:
data.drop_duplicates()
- Transformation Using Functions/Mapping: Modifying data values.
- Example:
data['column'].apply(lambda x: x * 2)
- Replacing Values: Substituting specific values.
- Example:
data['column'].replace({'old_value': 'new_value'})
- Handling Missing Data: Filling or dropping missing values.
- Example:
from sklearn.impute import SimpleImputer
4. Types of Data Analytics Model
- Predictive Analytics: Predicts future outcomes.
- Descriptive Analytics: Summarizes past data.
- Prescriptive Analytics: Recommends actions.
5. Association Rules
- Apriori Algorithm: Finds frequent itemsets, generates rules.
- Example:
from mlxtend.frequent_patterns import apriori
- FP-Growth Algorithm: Efficient itemset mining without candidate generation.
6. Regression
- Linear Regression: Models relationship between variables.
- Example:
from sklearn.linear_model import LinearRegression
- Logistic Regression: Binary classification.
- Example:
from sklearn.linear_model import LogisticRegression
7. Classification
- Naive Bayes: Probabilistic classifier based on Bayes' theorem.
- Example:
from sklearn.naive_bayes import GaussianNB
- Decision Trees: Tree-like model for decisions.
- Example:
from sklearn.tree import DecisionTreeClassifier
8. Introduction to Scikit-learn
- Installation:
pip install scikit-learn
- Dataset: Loading datasets like Iris.
- Example:
from sklearn.datasets import load_iris
- Matplotlib: Visualization.
- Example:
import matplotlib.pyplot as plt
- Filling Missing Values: Using
SimpleImputer.
- Example:
from sklearn.impute import SimpleImputer
- Regression/Classification with Scikit-learn:
- Linear Regression:
from sklearn.linear_model import LinearRegression
- Logistic Regression:
from sklearn.linear_model import LogisticRegression
- Naive Bayes:
from sklearn.naive_bayes import GaussianNB
- Decision Trees:
from sklearn.tree import DecisionTreeClassifier
Summary
- Predictive Analytics: Use historical data to forecast future.
- Essential Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn.
- Data Preprocessing: Clean, transform, and prepare data.
- Data Analytics Models: Predictive, Descriptive, Prescriptive.
- Key Algorithms: Apriori, FP-Growth, Linear Regression, Logistic Regression, Naive Bayes, Decision Trees.
- Scikit-learn: Installation, dataset handling, missing values, implementing models.
Next Steps
- Advanced Topics: Deep Learning, Time Series Analysis, NLP, Big Data Analytics, Data Visualization.
- Projects: Apply skills in comprehensive data analytics projects.