Update README.md

README.md

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-license: odbl
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+---
+license: odbl
+---
+# Predicting Whether a Nuclear Reactor Is PWR or BWR
+
+## Dataset Overview
+
+This project is based on the **Geo Nuclear Data** dataset from Kaggle.  
+The dataset contains information about nuclear reactors around the world, including geographic and technical features such as:
+
+- Country
+- Plant name
+- Latitude
+- Longitude
+- Reactor type
+- Capacity
+- Status
+- Construction and operational dates
+
+The original dataset was cleaned and filtered in order to focus only on two reactor types:
+
+- **PWR**
+- **BWR**
+
+## Research Question
+
+**Can we predict whether a reactor is PWR or BWR?**
+
+## Data Wrangling and Cleaning
+
+After cleaning, the final dataset contained **573 rows**.
+
+## Descriptive Statistics and Main Notes
+
+The cleaned dataset still showed class imbalance:
+
+- **PWR:** 483 reactors
+- **BWR:** 120 reactors
+
+This means the dataset contains many more PWR reactors than BWR reactors, which may affect classification performance.
+
+## Research Questions, Visualizations, and Answers
+
+### Question 1: Is there a difference in capacity between PWR and BWR reactors?
+
+This question was explored using a bar chart comparing the **mean** and **median** capacity of each reactor type.
+
+**Answer:**  
+Yes. PWR reactors generally show higher capacity values than BWR reactors.  
+Both the average and the median capacity are higher for PWR reactors, although the number of PWR reactors in the dataset is also much larger.
+
+### Question 2: Are PWR and BWR reactors distributed differently across geographic locations?
+
+This question was explored using a **world map** based on latitude and longitude.
+
+**Answer:**  
+The map shows that nuclear reactors are highly concentrated near coastlines.  
+There is some geographic difference between PWR and BWR reactors, but not a perfect separation.  
+For example, many reactors in Russia and nearby regions appear to be PWR, and China also shows many PWR reactors.  
+# in general i see that there is two teams here, team "washington" and team "russia/china" it's not super obvies but we can defenetly can see it from the map. 
+# maybe i shuld improve my model by consider in which "team" the reactor is located.
+
+### Question 3: Which countries contain the highest numbers of PWR and BWR reactors?
+
+This question was explored using a **stacked bar chart** of reactor counts by country and reactor type.
+
+**Answer:**  
+The United States and China appear to have the highest numbers of reactors, followed by countries such as Japan and France. russia is surprisingly low maybe because of  chernobyl  
+The distribution of PWR and BWR reactors is not the same in all countries, which suggests that country may be useful as a predictive feature.
+
+### Question 4: How does the total global capacity compare between PWR and BWR reactors?
+
+This question was explored using a **bar chart** of total capacity by reactor type.
+
+**Answer:**  
+PWR reactors contribute much more total global capacity than BWR reactors in this dataset.  
+This matches the earlier findings that PWR reactors are both more common and usually larger in capacity.
+
+## Main Insights
+
+The main findings of the exploratory analysis were:
+
+- **Capacity** was the most informative feature in the dataset.
+- Geographic location was useful, but not strong enough on its own.
+- There was no single feature that perfectly separated PWR and BWR reactors.
+- The classification task seems to depend on a **combination of several features together**, not on one perfect variable.
+
+## Decisions Made During the Analysis
+
+Several important decisions were made during the project:
+
+- I decided to focus only on **PWR** and **BWR** reactors.
+- I removed rows with missing values in important columns.
+- I removed rows with missing coordinates because location was relevant to the research question.
+- I filled missing values in `Capacity` using the median.
+- I checked skewness in `Capacity`, but did not apply transformation because the skewness was moderate and not extreme.
+- I compared several models before selecting the final one.
+
+## Modeling
+
+The target variable was:
+
+- **ReactorType**
+  - `0 = BWR`
+  - `1 = PWR`
+
+The main engineered and selected features included:
+
+### Numeric features
+- Capacity
+- Latitude
+- Longitude
+- AbsLatitude
+- ConstructionYear
+- OperationalYear
+- YearsToOperation
+- ReactorAge
+
+### Categorical features
+- Status
+- Country
+- CountryCode
+
+The following models were tested:
+
+- Logistic Regression
+- Balanced Logistic Regression
+- Random Forest
+- Gradient Boosting
+
+## Model Results
+
+The best model was **Random Forest**.
+
+### Best Model: Random Forest
+
+- **Accuracy:** 0.8957
+- **Macro F1-score:** 0.8467
+- **BWR Recall:** 0.7917
+- **PWR Recall:** 0.9231
+
+**Interpretation:**  
+Random Forest performed better than the other models because it was able to capture more complex patterns in the data.  
+It also gave the best balance between overall performance and detection of both reactor types.
+
+## Limitations
+
+This project has several limitations:
+
+- Some useful variables originally had missing values.
+- The dataset is imbalanced, with many more PWR reactors than BWR reactors.
+- Geographic coordinates alone do not clearly separate the two reactor types.
+- Some interesting geographic or political patterns may exist, but they would require further research beyond this dataset.
+
+## Final Conclusion
+
+This project showed that it is possible to predict whether a nuclear reactor is PWR or BWR with good performance.
+
+The exploratory analysis showed that **Capacity** is the strongest single feature, while geographic location and country also provide useful information.  
+However, no single feature perfectly separates the two classes.
+
+Among all tested models, **Random Forest** gave the best results and was selected as the final model.