# Task-2-SALES-PREDICTION-USING-PYTHON
SALES PREDICTION USING PYTHON
Sales prediction involves forecasting the amount of a product that customers will purchase, taking into account various factors such as advertising expenditure, target audience segmentation, and advertising platform selection. In businesses that offer products or services, the role of a Data Scientist is crucial for predicting future sales. They utilize machine learning techniques in Python to analyze and interpret data, allowing them to make informed decisions regarding advertising costs. By leveraging these predictions, businesses can optimize their advertising strategies and maximize sales potential. Let's embark on the journey of sales prediction using machine learning in Python.
Jupyter Notebooks or any Python IDE. Pandas for data manipulation. Matplotlib and Seaborn for data visualization
Scikit-learn library for implementing machine learning algorithms. Commonly used models include Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, etc. Time series models (e.g., ARIMA or SARIMA) for predicting sales over time. Regression models if you have additional features influencing sales. Scikit-learn library for implementing machine learning algorithms. Common classification models like Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), or Neural Networks.
