MM - mat plotlib, filling missing values
Creating mind maps is an excellent way to visually organize and recall information. Here are the key concepts and short sentences for each section to help you create effective mind maps.
Predictive Data Analytics with Python
1. Introduction
- Definition: Using historical data to predict future events.
- Importance: Informs decision-making, risk management.
2. Essential Python Libraries
- NumPy: Numerical operations.
- Pandas: Data manipulation.
- Matplotlib & Seaborn: Data visualization.
- Scikit-learn: Machine learning tools.
3. Basic Examples
- Reading Data: Using
pd.read_csv(). - Data Operations: Filter, sort, group with Pandas.
4. Data Preprocessing
- Removing Duplicates:
data.drop_duplicates(). - Transformation: Apply functions/mapping.
- Replacing Values:
data.replace(). - Handling Missing Values: Fill, drop, predict.
5. Types of Data Analytics
- Predictive: Forecast future events.
- Descriptive: Summarize past data.
- Prescriptive: Recommend actions.
6. Key Algorithms
- Association Rule Learning: Relationships between variables.
- Apriori: Frequent itemsets, rules.
- FP-Growth: Efficient itemsets.
- Regression Analysis:
- Linear Regression: Continuous outcome prediction.
- Logistic Regression: Binary classification.
- Classification:
- Naive Bayes: Probabilistic, assumes independence.
- Decision Trees: Tree-like model decisions.
7. Introduction to Scikit-learn
- Installation:
pip install scikit-learn. - Datasets: Built-in datasets, e.g., Iris.
- Math Library: Utilizes NumPy.
- Handling Missing Values:
SimpleImputer. - Regression & Classification:
LinearRegression,DecisionTreeClassifier.
Matplotlib
1. Key Features
- Line Plots: Trends over time.
- Bar Charts: Compare groups.
- Histograms: Data distribution.
- Scatter Plots: Variable relationships.
- Pie Charts: Parts of a whole.
2. Basic Usage
- Line Plot:
plt.plot(). - Bar Chart:
plt.bar(). - Histogram:
plt.hist(). - Scatter Plot:
plt.scatter(). - Pie Chart:
plt.pie().
Filling Missing Values
1. Strategies
- Mean Imputation:
SimpleImputer(strategy='mean'). - Median Imputation:
SimpleImputer(strategy='median'). - Mode Imputation:
SimpleImputer(strategy='most_frequent'). - Forward Fill:
data.fillna(method='ffill'). - Backward Fill:
data.fillna(method='bfill'). - Interpolate:
data.interpolate().
Use these keywords and short sentences to create nodes and branches in your mind map. This will help you visually organize the information and make it easier to recall during your studies or exams.