Sales Prediction Using Python and Machine Learning

Sales Prediction means predicting how much of a product people will buy based on factors such as the amount you spend to advertise your product, the segment of people you advertise for, or the platform you are advertising on about your product.Machine learning algorithms can be used to create a sales forecasting model that can predict sales on a certain day after being provided with a certain set of inputs. Here is a detailed README file on sales prediction using Python and machine learning:

Required Libraries

To create a sales prediction model using Python and machine learning, you will need to install the following libraries:

• Numpy
• Pandas
• Sklearn
• Scipy
• Seaborn
• Keras
• Tensorflow

You can install these libraries using pip by running the following command:

pip install numpy pandas sklearn scipy seaborn keras tensorflow

Preparing the Data

Before training a machine learning model, the data needs to be preprocessed to be understandable by the machine. This includes cleaning the data, handling missing values, and encoding categorical variables.

Training the Model

Once the data is prepared, you can use a machine learning algorithm such as linear regression to train a sales prediction model. Here is an example of how to train a linear regression model using scikit-learn:

from sklearn.linear_model import LinearRegression

# Split the data into training and test sets
train_data = data.sample(frac=0.8, random_state=0)
test_data = data.drop(train_data.index)

# Separate the features and target variable
train_features = train_data.drop(['Weekly_Sales'], axis=1)
train_labels = train_data['Weekly_Sales']
test_features = test_data.drop(['Weekly_Sales'], axis=1)
test_labels = test_data['Weekly_Sales']

# Train the model
model = LinearRegression()
model.fit(train_features, train_labels)

Making Predictions

After training the model, you can use it to make predictions on new data. Here is an example of how to make predictions using the trained linear regression model:

# Make predictions on test data
predictions = model.predict(test_features)

# Calculate the mean absolute error
mae = np.mean(abs(predictions - test_labels))
print('Mean Absolute Error:', mae)