Week 4 Project:
Using the Craigslist Vehicles Dataset available on Kaggle (https://www.kaggle.com/datasets/mbaabuharun/craigslist-vehicles), we'd like you to create a Time-Series Model following the approach outlined below.
Here are the key steps:
Start by addressing missing values in the dataset. You can handle this by filling in missing values with the median for numerical columns and the mode for categorical columns. Ensure that the data types of the columns are appropriate. Specifically, make sure to convert the 'posting_date' column to a datetime data type. Utilize the 'posting_date' column to create a datetime index for the dataset. This will facilitate the analysis of temporal patterns. With clean data, explore it using various visualizations and statistical analysis techniques. This step is crucial for understanding temporal patterns, identifying seasonal trends, and analyzing demand-supply dynamics by region and vehicle type. Build the time-series chart. Finally, create a GitHub Repository and push your work there, also document your process through each of the steps and demonstrate your understanding by implementing them on the dataset.
-
Conclusions:
-
Trend Observation: The time series chart demonstrates price fluctuations over the specified time period, which is important to monitor and understand.
-
Anomaly Detection: Notably, there was a sharp increase in price from 10 to 15 on April 16, 2021, followed by fluctuations, including a drop to 8 on April 23, 2021. These fluctuations suggest anomalies or unusual events during this time.
-
Recommendations: Continuous Monitoring on price over time to identify and respond to trends or anomalies promptly.