Data Cleaning and Strategic Gap Filling for a Local Market Dataset
Maksettu toimituksen yhteydessä
I am seeking a skilled data scientist or data analyst with expertise in professional and scientific data management techniques to assist in completing and filling the missing values in a daily local market oil price dataset. The dataset contains crucial information on diesel, petrol, and LPG prices collected from two distinct locations: Hairatan and Aqena, each with slight variations in fuel prices. The dataset exhibits both small, short-term data gaps and more substantial, extended gaps that require specialized attention.
• Type of Data: Daily local market oil price data for diesel, petrol, and LPG.
• Data Sources: Collected from two different locations, Hairatan and Aqena (two local fuel markets).
• Variations: Prices differ between the two markets.
• Data Volume: approximately 8,760 entries, encompassing daily records for six distinct variables over a four-year period.
Data Gap Types:
1. Short Gaps: These are small, brief periods of missing data.
2. Extended Gaps: These are larger, more prolonged periods of missing data.
Approaches to Fill Data Gaps:
1. Conventional Technique: For short gaps, conventional data management techniques will be applied to fill in the missing values. This might include interpolation, time-series methods, or other suitable approaches.
2. Comparative Method: For extended gaps, it's acknowledged that filling the missing data by looking at prior and subsequent data points of the same variable may not yield accurate results. Instead, we propose using a comparative method that leverages data from the adjacent market (Hairatan or Aqena) to fill the gap.
• Market Comparison: It will be essential to establish a relationship between price changes in the two markets to make informed assumptions for gap filling when data from the adjacent market is available.
• Accuracy: Ensuring accuracy in price predictions is paramount, especially for extended gaps.
• Completed dataset with missing values filled using appropriate techniques.
• Documentation detailing the methods and approaches used for gap filling.
• Visualizations or reports showcasing the filled data and insights gained.
Attachment: A screenshot illustrating sample longer missing data gaps and short gaps is included for reference.
Budget and Timeline:
• Budget: Kindly submit your precise and realistic bid amount. We do not engage with placeholder or fictitious price offers. Your accuracy is appreciated.
• Timeline: The project timeline is negotiable, but timely completion is essential.
Projektin tunnus: #37239070
40 freelanceria on tarjonnut keskimäärin $138 tähän työhön
I am an expert Swift coder with skills including Data Management, Data Analysis, Data Cleansing, Data Science and Statistics. Please send a message to discuss more about this project. Thanks & regards