"Data Prep: Clean, Normalize, Transform"

Step 1: Dealing with duplicates, missing values, and outliers

1. Duplicates: Check if there are any identical rows in your dataset. If duplicates are found, you can remove them to ensure each observation is unique.
2. Missing Values: Identify columns with missing data. Decide how to handle them:Remove: If only a small percentage of data is missing and it won't impact the analysis.Impute: Fill in missing values using methods like mean, median, mode, or more advanced techniques like regression or k-nearest neighbors.
3. Outliers: Outliers are extreme values that can skew your analysis. Detect outliers using methods like the Z-score or the Interquartile Range (IQR). Decide whether to remove, transform, or keep them based on domain knowledge.

Step 2: Data normalization and standardization

1. Normalization: This involves scaling numerical features to a similar range, usually between 0 and 1. It's useful when features have different units or scales.
2. Standardization: This centers the data around zero with a standard deviation of 1. It's useful when features have different distributions. Standardization retains information about outliers better than normalization.

Step 3: Feature scaling and transformation

1. Feature Scaling: Ensures all features have comparable scales, preventing one feature from dominating others during analysis. Common methods include normalization and standardization.
2. Feature Transformation: Some algorithms assume a certain distribution of data, like a normal distribution. Transformation methods like logarithm, square root, or Box-Cox can help make data more closely resemble a desired distribution.

Remember, data cleaning and preprocessing are crucial for building accurate and effective machine learning models. These steps ensure that your data is in good shape for analysis and modeling, leading to more reliable insights and predictions.

EXAMPLE CODE:

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import PowerTransformer
from scipy import stats

# Step 1: Dealing with duplicates, missing values, and outliers
# Load your dataset
data = pd.read_csv('your_dataset.csv')

# Remove duplicates
data = data.drop_duplicates()

# Handle missing values
imputer = SimpleImputer(strategy='mean')
data_filled = imputer.fit_transform(data)

# Detect and handle outliers using Z-score
z_scores = stats.zscore(data_filled)
data_no_outliers = data_filled[(z_scores < 3).all(axis=1)]

# Step 2: Data normalization and standardization
# Normalization
scaler_norm = MinMaxScaler()
data_normalized = scaler_norm.fit_transform(data_no_outliers)

# Standardization
scaler_std = StandardScaler()
data_standardized = scaler_std.fit_transform(data_no_outliers)

# Step 3: Feature scaling and transformation
# Transformation (using Box-Cox)
pt = PowerTransformer(method='box-cox')
data_transformed = pt.fit_transform(data_standardized)

# Now you can use the 'data_transformed' for analysis or modeling