Add New snippets Codes ...

Author: aminzayer

MostCommonDataScienceGist.py

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+# 10 common Python code snippets used in data science
+# These code snippets cover various aspects of data science, including data loading, exploration, visualization, cleaning, machine learning modeling, evaluation, and feature selection. You can adapt and expand upon these snippets to suit your specific data science projects.
+# 1. Importing Libraries:
+
+# Import commonly used data science libraries
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Description: Import essential data science libraries like NumPy, Pandas, Matplotlib, and Seaborn.
+# -------------------------------------------------------------------------------------------------------------------------
+# 2. Loading Data:
+
+# Load a CSV file into a Pandas DataFrame
+data = pd.read_csv('data.csv')
+
+# Description: Load data from a CSV file into a Pandas DataFrame for analysis.
+# -------------------------------------------------------------------------------------------------------------------------
+#3. Data Exploration:
+
+# Display basic statistics of the DataFrame
+print(data.describe())
+
+# Description: Get a summary of the data, including mean, standard deviation, and other statistical measures.
+# -------------------------------------------------------------------------------------------------------------------------
+# 4. Data Visualization:
+
+# Create a histogram to visualize a numeric variable
+plt.hist(data['column_name'], bins=20)
+plt.xlabel('X-axis Label')
+plt.ylabel('Y-axis Label')
+plt.title('Histogram of Column')
+plt.show()
+
+# Description: Create a histogram to visualize the distribution of a numeric variable.
+# -------------------------------------------------------------------------------------------------------------------------
+# 5. Data Cleaning:
+
+# Check for and handle missing values
+data.isnull().sum()
+data.fillna(value, inplace=True)
+
+# Description: Identify and handle missing values in the dataset.
+# -------------------------------------------------------------------------------------------------------------------------
+# 6. Data Preprocessing:
+
+# Encode categorical variables using one-hot encoding
+data = pd.get_dummies(data, columns=['categorical_column'])
+
+# Description: Convert categorical variables into numerical format using one-hot encoding.
+# -------------------------------------------------------------------------------------------------------------------------
+# 7. Machine Learning Model:
+
+# Train a machine learning model (e.g., Linear Regression)
+from sklearn.linear_model import LinearRegression
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Description: Train a machine learning model, such as linear regression, on a training dataset.
+# -------------------------------------------------------------------------------------------------------------------------
+# 8. Model Evaluation:
+
+# Evaluate the model's performance using metrics like RMSE
+from sklearn.metrics import mean_squared_error
+y_pred = model.predict(X_test)
+rmse = np.sqrt(mean_squared_error(y_test, y_pred))
+print('Root Mean Squared Error:', rmse)
+
+# Description: Assess the model's performance using evaluation metrics like RMSE.
+# -------------------------------------------------------------------------------------------------------------------------
+# 9. Data Splitting:
+
+# Split the dataset into training and testing sets
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Description: Divide the data into training and testing subsets for model validation.
+# -------------------------------------------------------------------------------------------------------------------------
+# 10. Feature Selection:
+
+# Select important features using feature importance
+from sklearn.ensemble import RandomForestClassifier
+model = RandomForestClassifier()
+model.fit(X, y)
+feature_importance = model.feature_importances_
+
+# Description: Determine important features in a dataset using techniques like feature importance.
+# -------------------------------------------------------------------------------------------------------------------------