How to utilize Python in analyzing the big dataset?

To handle your scenario you can use Python to manage, clean, and extract meaningful insights from big datasets. Here are the uses of Python given below in extracting things from your datasets:-

Data loading and cleaning You can use pandas which is a library of Python to load the dataset into a data frame of pandas. It can help you in cleaning and removing duplicates. Here is the example given:-

Import pandas as pd

# Load the data into a Pandas DataFrame

Data = pd.read_csv(‘sales_data.CSV)

# Handling missing values

Data. drop(inplace=True)

# Removing duplicates

Data.drop_duplicates(inplace=True)racetandardizing formats (e.g., converting date columns to datetime foformat)

Data[‘date_column’] = pd.to_datetime(data[‘date_column’])

Exploratory Data analysis

You can use Pandas, marplot, and Seaborn to visualize the data. Here is the example given

Import matplotlib. pyplot as plt

# Visualizing sales trends over time

Plt. figure(figsize=(10, 6))

Plt. plot(data[‘date_column’], data[‘sales_column’])

Plt. label(‘Date’)

Plt. label(‘Sales’)

Plt. title(‘Sales Trends over Time)

Plt. show()

Future Engineering and Transformation

Using Python, you can create advanced new features to extract more meaningful insights from databases. Here is how you can

# Extracting month and year from the date column

Data[‘month’] = data[‘date_column’].dt.month

Data[‘year’] = data[‘date_column’].dt.year

# Feature scaling or normalization if needed

From sklearn.preprocessing import MinMaxScaler

Scaler = MinMaxScaler()

Data[[‘sales_column’]] = scaler.fit_transform(data[[‘sales_column’]])

Model building and prediction

You can also gain model building and prediction by using Python to gain insights into customer's behavior based on the data. Here is an example

From sklearn.model_selection import train_test_split

From sklearn.linear_model import LinearRegression

# Splitting the data into train and test sets

X = data[[‘month’, ‘year’]]  # Features

Y = data[‘sales_column’]  # Target variable

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Training a linear regression model

Model = LinearRegression()

Model. fit(X_train, y_train)

# Making predictions

Predictions = model. predict(X_test)

Evaluation You can assess and evaluate the performance to derive insights to support decision-making by using Python

From sklearn.metrics import mean_squared_error, r2_score

# Evaluating the model

Mse = mean_squared_error(y_test, predictions)

R2 = r2_score(y_test, predictions)

Print(f”Mean Squared Error: {mse}”)

Print(f”R-squared: {r2}”)

# Extracting feature importance

Feature_importance = model.coef_

Print(f”Feature Importance: {feature_importance}”)

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