Data Cleaning

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• [x] Replace missing values with the mode or median on a column-wise basis.
• [x] Encode categorical variables using appropriate methods.
• [x] Remove rows with missing values.
• [ ] Automatic discovery and correction of common data issues such as outliers and format errors can be achieved using various data preprocessing techniques and libraries in Python. Here's a general approach:

Data Cleaning:

• [x] Outlier Detection: Use statistical methods such as Z-score, IQR (Interquartile Range), or machine learning algorithms to identify outliers in numerical data. Once identified, you can choose to remove them or replace them with a more suitable value (e.g., mean, median, or a predefined threshold).

• [x] Format Errors: For format errors in categorical or textual data, you can perform string normalization, handle missing values, and correct typos using techniques such as fuzzy matching, regular expressions, or string similarity measures.

Data Transformation:

• [x] Normalization: Scale numerical features to a standard range (e.g., between 0 and 1) to make them comparable. Techniques like Min-Max scaling or Z-score normalization can be used.

• [x] Encoding Categorical Variables: Convert categorical variables into numerical format using techniques like one-hot encoding or label encoding.

Data Imputation:

• [x] Missing Values: Handle missing values in the dataset by imputing them with appropriate replacements. This can be done using methods like mean, median, mode imputation, or more advanced techniques like KNN imputation or predictive modeling.

Feature Engineering:

• [ ] Derived Features: Create new features that capture useful information from existing ones. For example, extracting date components (year, month, day) from a datetime feature or creating interaction terms between numerical variables.

Data Validation:

• [ ] Schema Validation: Validate the data against predefined schemas to ensure it meets the expected structure and format.

• [ ] Cross-Field Validation: Perform checks across multiple fields to identify inconsistencies or errors in the data.

Data Visualization:

• [ ] Use data visualization techniques such as histograms, box plots, scatter plots, and heatmaps to explore the data distribution, identify patterns, and detect anomalies visually.

Machine Learning Techniques:

• [x] Utilize machine learning models for anomaly detection, data imputation, and outlier detection. Algorithms like Isolation Forest, Local Outlier Factor (LOF), and Autoencoders can be effective for detecting anomalies in the data. Iterative Process:

Data cleaning and preprocessing are often iterative processes. After applying initial techniques, it's essential to evaluate the results, refine the approach, and iterate until satisfactory results are achieved.

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Clean data = cleaned_data.csv Encoded data = encoded_data.csv

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Do this for Outlier Detection, and compare what's best:

There isn't a single machine learning algorithm specifically designed to identify outliers. Outlier detection is typically considered as an unsupervised learning problem, where the goal is to identify observations that significantly deviate from the majority of the data points. Here are a few common approaches used in machine learning for outlier detection:

Statistical Methods: These methods rely on statistical properties of the data, such as mean, median, standard deviation, and quartiles. Common statistical methods include Z-score, modified Z-score, and Interquartile Range (IQR).

Distance-based Methods: These methods detect outliers based on the distance between data points. Common distance-based methods include k-nearest neighbors (kNN) and Local Outlier Factor (LOF).

Density-based Methods: These methods identify outliers as data points located in low-density regions. Density-based methods include DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and Mean Shift clustering.

Isolation Forest: This is an ensemble method that isolates outliers by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

One-Class SVM: This method learns a decision boundary around the majority of the data points and identifies outliers as data points lying outside this boundary.

Autoencoder Neural Networks: Autoencoders are neural networks trained to reconstruct the input data. Outliers can be identified based on the reconstruction error, where higher errors indicate outlier data points.

Each of these methods has its advantages and is suitable for different types of data and outlier distributions. It's often recommended to try multiple methods and compare their performance on a given dataset.