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- Here’s a brief theoretical explanation for all 12 experiments, including relevant libraries and key features:
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- ### **Experiment 1: Data Types and List Operations**
- - **Data Types**: Python supports built-in types like Numeric, Sequence (string, list, tuple), Boolean, Set, and Dictionary. Type conversion allows switching between types (e.g., `int()` to `float()`).
- - **Lists**: Used to store multiple items in one variable. Operations include creation, appending, slicing, indexing, and reversing.
- - **Key Feature**: Lists are mutable, meaning they can be altered after creation.
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- ### **Experiment 2: Dictionary Operations**
- - **Dictionary**: A collection of key-value pairs enclosed in `{}`. Keys are immutable and unique, while values can be mutable and duplicated.
- - **Operations**: Create, append values, update existing keys, and print key-value pairs.
- - **Key Feature**: Efficient for lookups and mutable, allowing dynamic modifications.
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- ### **Experiment 3: NumPy Arrays and Data Import/Export**
- - **Library**: `NumPy` (Numerical Python) provides powerful tools for handling large numerical datasets and multidimensional arrays (`ndarray`).
- - **Array Operations**: Perform mathematical operations like addition, subtraction, multiplication, and division.
- - **File Handling**: `pandas` enables data import (`read_csv`) and export (`to_csv`).
- - **Key Feature**: NumPy arrays are faster and consume less memory than Python lists.
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- ### **Experiment 4: Pandas DataFrames and Matplotlib Visualization**
- - **Pandas**: A library for data manipulation, with `DataFrame` (2D tabular data structure) as its core feature.
- - **Matplotlib**: A plotting library used for creating graphs (line, bar, scatter, histogram).
- - **Key Feature**: Integration of Pandas and Matplotlib simplifies visualization of DataFrame data.
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- ### **Experiment 5: Statistical Computations**
- - **Statistics**: Calculate sum, mean, and standard deviation using `NumPy` functions like `np.sum()`, `np.mean()`, and `np.std()`.
- - **Key Feature**: Efficiently handle statistical computations for numerical data.
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- ### **Experiment 6: Handling Missing Data**
- - **Handling Missing Values**: Use `isnull()` and `notnull()` in `pandas` to detect missing values, then replace them using methods like `fillna()`.
- - **Key Feature**: Ensures data integrity by handling gaps caused by missing information.
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- ### **Experiment 7: Normalization and Standardization**
- - **Normalization**: Rescales data to fit within a range, usually [0, 1], using Min-Max scaling.
- - **Standardization**: Converts data to a standard scale using Z-score normalization (`(x - mean) / std`).
- - **Key Feature**: Used in machine learning to prevent features with larger ranges from dominating others.
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- ### **Experiment 8: Data Preprocessing**
- - **Data Preprocessing**: Techniques like handling missing values, outlier removal, and feature scaling (e.g., Min-Max scaling or standardization).
- - **Library**: Use `pandas` for cleaning data and `scipy` or `numpy` for outlier handling.
- - **Key Feature**: Improves data quality for analysis or machine learning tasks.
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- ### **Experiment 9: Skewness and Kurtosis**
- - **Skewness**: Measures asymmetry in the distribution of data. Positive skew has a longer right tail; negative skew has a longer left tail.
- - **Kurtosis**: Measures the "tailedness" of the distribution. High kurtosis indicates heavy tails; low kurtosis indicates light tails.
- - **Library**: Use `scipy.stats` for calculations.
- - **Key Feature**: Provides insights into data distribution beyond mean and variance.
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- ### **Experiment 10: Feature Selection using ANOVA**
- - **ANOVA (Analysis of Variance)**: Compares means of three or more groups to determine if at least one mean is different.
- - **Library**: `scipy.stats.f_oneway` conducts one-way ANOVA.
- - **Key Feature**: Identifies significant features in datasets for predictive modeling.
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- ### **Experiment 11: Heatmap for Correlation**
- - **Heatmap**: A graphical representation of data where individual values are represented by color.
- - **Library**: `seaborn` for creating heatmaps and `matplotlib` for displaying.
- - **Correlation**: Measures the linear relationship between variables. Values range from -1 (perfect negative) to +1 (perfect positive).
- - **Key Feature**: Quickly visualizes variable relationships.
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- ### **Experiment 12: Regression, Classification, and Confusion Matrix**
- - **Regression**: Predicts continuous values (e.g., house prices) using models like Linear Regression.
- - **Classification**: Predicts categorical values (e.g., Iris species) using algorithms like Random Forest.
- - **Confusion Matrix**: Evaluates the performance of classification models by showing true positives, true negatives, false positives, and false negatives.
- - **Libraries**: `sklearn` for building models and calculating metrics, `matplotlib` for visualizations.
- - **Key Feature**: Combines supervised learning techniques for prediction and evaluation.
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- This concise overview highlights the core concepts and tools used in each experiment. Let me know if you'd like further elaboration on any specific experiment!
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