Analysis of Coca-Cola Inc. (KO) daily OHLCV data highlights a robust 60-year growth path (1962 to 2022) that evolved from sparse trading volumes to elevated monthly averages. It narrows down the trends to show steady performance over decades. Power BI visuals use optimized charts to track price and volume growth by month, quarter, and year, and KPI cards to highlight win rates and returns.
-
🗄️ dataset – Raw dataset and cleaned CSV files
-
📈 analysis charts – Final analysis visuals (PNG)
-
⚙️ process charts – Technical work steps visual (PNG)
-
💡 insights.md – All insights from analysis
-
📉 Analysis Findings – All metrics and findings from the analysis
-
📖 README.md – Project overview
-
Excel – Initial dataset validation (rows/columns)
-
Power Query - Data cleaning and transformation
-
Power BI – Interactive visualizations
-
GitHub – Hosting documented projects
-
Markdown – Documentation (.md files)
- Power BI dashboard design for 60+ years of KO stock data trends
- DAX measure creation (AVERAGE, SUM, DIVIDE) for accurate aggregations
- Data transformation from raw to cleaned datasets
- Financial visualization of historical stock performance
- Problem-solving to match and analyse aggregation trends
| Q | Business Question | Visualizations Used |
|---|---|---|
| Q1 | Average KO volume by year | Clustered Column Chart |
| Q2 | Average KO volume by month | Clustered Column Chart |
| Q3 | Average KO close price by year | Area Chart |
| Q4 | Average KO close price by quarter | Clustered Column Chart |
| Q5 | Average KO close price by month | Clustered Column Chart |
Coca-Cola (KO) offers unmatched reliability: ~48% daily wins, 1.74% low volatility, 152,050% growth (0.04 to 60.86), and ±5% steady volume driven by recession-proof beverage demand, pricing power, and globally accepted brand.
See Insights Here
key data points and metrics extracted from the KO analysis. See Findings Here
This folder contains all the final charts and visualisations generated by this analysis. These include resistance trends over time, comparisons by gender, age group, and bacterial species. View charts here: See Analysis Charts Here
Work-in-progress charts showing screenshots of analysis steps. See Process Charts Here
Raw Dataset – Original KO stock data (1962-2022): open, high, low, close prices + daily volume (~15K trading days).
Cleaned Dataset – Processed CSVs with optimized data types for Power BI analysis. See Datasets Here
Coca-Cola Stock Dataset (1962–2022) by Kalilur Rahman (Kaggle). Dataset Source
Analysis of Coca-Cola Inc. (KO) daily OHLCV data highlights a robust 60-year growth path (1962 to 2022) that evolved from sparse trading volumes to elevated monthly averages. It narrows down the trends to show steady performance over decades. Power BI visuals use optimized charts to track price and volume growth by month, quarter, and year, and KPI cards to highlight win rates and returns.
The objective of this analysis is to assess how fuel prices, tax percentages, subsidy support, and income levels shape fuel affordability across different continental regions and countries from 2020 to 2026, and to highlight the regions and countries most affected by affordability pressure.
- Average KO volume by year.
- Average KO volume by month.
- Average KO close price by year.
- Average KO close price by quarter.
- Average KO close price by month.
- Excel (Initial data preview and quick validation of rows & columns distributions)
- Power Query (Data cleaning and preparation)
- Power BI (Analysis visualization)
Coca-Cola Stock Dataset (1962–2022) by Kalilur Rahman (Kaggle). Dataset Source
- Standardized column data types for consistency and analysis.
- Checked for duplicates (none were found.)
- Checked for missing values (none were found.)
- Renamed Open, High, Low, and Close with a Price prefix for clarity.
- Created key measures such as Average Close Price, Average Daily Volatility, Total Trading Days, and related metrics.
- Volume of KO Traded – This analysis shows the total trading activity of KO over the selected period, helping to indicate overall market and liquidity.
- Total Trading Days – This analysis represents the total number of trading days covered in the dataset, showing the length of the period under review.
- Close > Open (%) – This metric shows the percentage of trading days when the closing price was higher than the opening price, reflecting how often KO ended the day positively.
- Average Daily Volatility – This metric measures the average amount KO’s price moved each day, showing how stable or volatile the stock was over the period.
- Average Volume by Year Analysis – This analysis examines how KO’s trading volume changed across the 60 years from 1962 to 2022.
- Average Volume by Month Analysis – This analysis examines how KO’s trading volume varies across different months of the year.
- Average Close Price by Year – This analysis examines KO’s average closing price over time to highlight the stock’s growth trend across the study period.
- Average Closing Price by Quarter – This analysis examines KO’s average closing price across each quarter, helping to reveal short-term performance trends within the year.
- Average Closing Price by Month – This analysis examines KO’s average closing price across each month, helping to identify recurring seasonal patterns and short-term price trends.
Average fuel prices varied widely by region, with Oceania and Europe recording the highest values overall. The Middle East had the lowest average fuel price, while South America and Africa also had relatively low prices.
Average tax percentages also differed noticeably by region. Oceania and Europe generally had the highest tax burdens, while South America had the lowest average tax percentage, followed by the Middle East.
The Middle East stands out with the highest average subsidy, and that matches its high share of Very High subsidy levels, while South America and Africa also show relatively stronger support than most regions. At the other end, Europe and especially Oceania have the lowest average subsidies, with Oceania being entirely in the Low subsidy category and Europe also heavily concentrated there. Overall, the pattern suggests that subsidy support is much stronger in the Middle East and weaker in Europe and Oceania.
Oceania is the most concentrated in the High category, with Europe and North America also leaning strongly toward High. In contrast, Africa is mostly in the Low category, and South America dominates the Middle level. Asia shows a more mixed split across the three categories, while the Middle East shows a split only between Middle and High, with High making up the larger portion.
Coca-Cola (KO) offers unmatched reliability: ~48% daily wins, 1.74% low volatility, 152,050% growth (0.04 to 60.86), and ±5% steady volume driven by recession-proof beverage demand, pricing power, and globally accepted brand. See Insights Here
- 📈 analysis charts – Final analysis visuals (PNG)
- 🗄️ dataset – Raw dataset and cleaned CSV files
- 📋 group tables – Pandas aggregated tables in CSV files
- ⚙️ process charts – Technical work steps visual (PNG)
- 📖 README.md – Project overview
- 🔎 Analysis Findings – All metrics and findings from the analysis
- 💡 insights.md – All insights from analysis