This dataset provides a comprehensive analysis of electric vehicle (EV) charging patterns and user behavior. It contains 1,320 samples of charging session data, including metrics such as energy consumption, charging duration, and vehicle details. Each entry captures various aspects of EV usage, allowing for insightful analysis and predictive modeling.

Key Features:

• User ID: Unique identifier for each user.
• Vehicle Model: Model of the electric vehicle being charged (e.g., Tesla Model 3, Nissan Leaf).
• Battery Capacity (kWh): Total battery capacity of the vehicle in kilowatt-hours.
• Charging Station ID: Unique identifier for the charging station used.
• Charging Station Location: Geographic location of the charging station (e.g., New York, Los Angeles).
• Charging Start Time: Timestamp indicating when the charging session began.
• Charging End Time: Timestamp indicating when the charging session ended.
• Energy Consumed (kWh): Total energy consumed during the charging session, measured in kilowatt-hours.
• Charging Duration (hours): Total time taken to charge the vehicle, measured in hours.
• Charging Rate (kW): Average power delivery rate during the charging session, measured in kilowatts.
• Charging Cost (USD): Total cost incurred for the charging session, measured in US dollars.
• Time of Day: Time segment when the charging occurred (e.g., Morning, Afternoon).
• Day of Week: Day of the week when the charging occurred (e.g., Monday, Tuesday).
• State of Charge (Start %): Battery charge percentage at the start of the charging session.
• State of Charge (End %): Battery charge percentage at the end of the charging session.
• Distance Driven (since last charge) (km): Distance traveled since the last charging session, measured in kilometers.
• Temperature (°C): Ambient temperature during the charging session, measured in degrees Celsius.
• Vehicle Age (years): Age of the electric vehicle, measured in years.
• Charger Type: Type of charger used (e.g., Level 1, Level 2, DC Fast Charger).
• User Type: Classification of user based on driving habits (e.g., Commuter, Long-Distance Traveler).
  This dataset is ideal for researchers, data scientists, and analysts interested in understanding electric vehicle charging behaviors and developing predictive models related to energy consumption and user patterns.
  please do not use this dataset for research purposes . It was primarily designed to implement machine learning algorithms and is not a reliable source for a paper or article.