springboard-curriculum · sanjaykshetri · Sep 7, 2025 · Sep 8, 2025 · Sep 11, 2025 · Sep 11, 2025
diff --git a/Notebooks/02_data_wrangling.ipynb b/Notebooks/02_data_wrangling.ipynb
diff --git a/Notebooks/02_data_wrangling1.ipynb b/Notebooks/02_data_wrangling1.ipynb
diff --git a/Notebooks/03_exploratory_data_analysis.ipynb b/Notebooks/03_exploratory_data_analysis.ipynb
diff --git a/Notebooks/03_exploratory_data_analysis1.ipynb b/Notebooks/03_exploratory_data_analysis1.ipynb
diff --git a/Notebooks/04_preprocessing_and_training.ipynb b/Notebooks/04_preprocessing_and_training.ipynb
diff --git a/Notebooks/04_preprocessing_and_training1.ipynb b/Notebooks/04_preprocessing_and_training1.ipynb
diff --git a/Notebooks/05_modeling.ipynb b/Notebooks/05_modeling.ipynb
diff --git a/Notebooks/summary_report1.ipynb b/Notebooks/summary_report1.ipynb
diff --git a/Notebooks/summary_report1.md b/Notebooks/summary_report1.md
@@ -0,0 +1,221 @@
+df.head()
+df['Amenity Count'].hist(bins=10)
+df.isnull().sum()
+
+# Big Mountain Resort: Ski Resort Pricing Analysis
+
+---
+
+## Quick Reference Summary
+
+| **Section**      | **Details**                                                                 |
+|------------------|-----------------------------------------------------------------------------|
+| **Problem**      | Current ticket price is below market; opportunity to optimize revenue        |
+| **Recommendation** | Raise adult weekend ticket price from **$81.00** to **$95.87**              |
+| **Impact**       | **18%** estimated annual revenue increase                                    |
+| **Action Plan**  | Implement new price, communicate value, monitor feedback, review annually   |
+| **Next Steps**   | Approve pricing, launch communications, track results                       |
+
+---
+
+## Executive Summary
+
+Big Mountain Resort is currently underpricing its adult weekend tickets compared to similar resorts. Our analysis recommends increasing the ticket price from **$81.00** to **$95.87**, which could increase annual revenue by an estimated **18%**. This adjustment aligns with market expectations and leverages the resort’s premium features, supporting both competitiveness and profitability.
+
+**Visual Summary:**
+
+```
+Current Price   : $81.00  |███████████████████
+Recommended     : $95.87  |███████████████████████
+```
+
+---
+
+## 1. Problem Identification
+
+**Objective:**  
+Big Mountain Resort wants to optimize its pricing strategy to remain competitive and maximize revenue.
+
+**Business Problem:**  
+- How do various features (location, amenities, size, etc.) influence ski resort pricing?
+- What pricing strategy should Big Mountain Resort adopt based on data-driven insights?
+
+**Data Context:**
+- Dataset includes over 100 ski resorts across 10 states, collected for the 2024-2025 season.
+- Features: Resort name, state, size, distance to nearest major city, amenities, and price.
+
+---
+
+## 2. Recommendation & Key Findings
+
+**Recommended Price:** **$95.87**  
+**Current Price:** **$81.00**  
+**Potential Revenue Increase:** **18%**
+
+Based on the predictive model, Big Mountain Resort should consider increasing its adult weekend ticket price from the current $81.00 to approximately $95.87. This adjustment would bring pricing in line with comparable resorts and is supported by market data. This represents a recommended increase of about $15 (or 19%).
+
+**Business Impact:**
+- Estimated annual revenue increase: **18%** (assuming stable attendance).
+- Improved market positioning and perceived value.
+
+**Customer Perspective:**
+- The recommended price remains competitive with similar resorts offering comparable amenities.
+- Enhanced amenities and experiences will support customer satisfaction and justify the price increase.
+
+**Action Plan:**
+1. Announce and implement the new ticket price for the upcoming season.
+2. Communicate the value of premium amenities in marketing materials.
+3. Monitor customer feedback and adjust as needed.
+4. Review pricing annually based on updated data and market trends.
+
+**Implementation Timeline:**
+
+| Step                        | Target Date         |
+|-----------------------------|--------------------|
+| Approve new pricing         | October 2025       |
+| Update website & materials  | November 2025      |
+| Launch communications       | November 2025      |
+| Implement new price         | December 2025      |
+| Review feedback & results   | March 2026         |
+
+**Risks & Mitigations:**
+- Risk: Potential customer pushback on price increase.
+  - Mitigation: Emphasize improvements and value in communications; offer early-bird or loyalty discounts.
+- Risk: Competitor price changes.
+  - Mitigation: Continue market monitoring and adjust pricing as needed.
+
+**Supporting Materials:**
+- [Full Jupyter Notebook (05_modeling.ipynb)](05_modeling.ipynb)
+- [Raw Data CSV](../raw_data/ski_resort_data.csv)
+- [Project Repository (GitHub)](https://github.com/sanjaykshetri/DataScienceGuidedCapstone2)
+
+**Key Findings:**  
+- Resort size, proximity to major cities, and premium amenities are the strongest predictors of higher pricing.
+- Big Mountain Resort is currently priced below comparable resorts with similar features.
+
+**Model Conclusion:**
+- The model predicts an optimal price of $95.87 for Big Mountain Resort, compared to the current price of $81.00.
+- This suggests the resort is underpricing its tickets and has an opportunity to increase revenue without risking competitiveness.
+
+**Operational Suggestions:**
+- Bundle premium amenities in marketing packages to increase perceived value.
+- Consider targeted marketing for resorts farther from cities, emphasizing unique experiences.
+
+---
+
+## 3. Data Overview
+
+_Example data:_
+
+| Resort Name      | State | Size | Distance to City | Amenities      | Price |
+|------------------|-------|------|------------------|---------------|-------|
+| Alpine Peaks     | CO    | Large| 120              | Spa, Night Ski| 150   |
+| Snowy Ridge      | UT    | Med  | 80               | Spa           | 120   |
+| Glacier Valley   | CA    | Large| 200              | Night Ski     | 180   |
+| ...              | ...   | ...  | ...              | ...           | ...   |
+
+---
+
+## 4. Modeling Results & Analysis
+
+**Key Steps:**
+- Cleaned missing values and standardized feature formats.
+- Engineered new features (e.g., amenities count, distance buckets).
+- Used a Random Forest model to predict optimal pricing.
+
+**Top Predictors:**
+- Resort Size
+- Distance to City
+- Number of Premium Amenities
+
+**Performance Metrics:**
+
+| Metric      | Value  |
+|-------------|--------|
+| R² Score    | 0.82   |
+| MAE         | $12.50 |
+| RMSE        | $18.30 |
+
+---
+
+## 5. Limitations & Next Steps
+
+- The model is based on available features; additional data (e.g., customer reviews, weather, seasonal trends) could improve accuracy.
+- Some amenities may be underreported or inconsistently labeled.
+- Future work: Integrate real-time demand data and competitor pricing APIs for dynamic pricing.
+
+---
+
+## 6. Summary & Conclusion
+
+- Data-driven pricing can increase revenue and competitiveness.
+- Big Mountain Resort should consider raising prices for premium offerings and investing in amenities.
+- Ongoing data collection and model updates are recommended for continued optimization.
+
+---
+
+## Executive Call to Action
+
+**Approve the new pricing strategy for the 2025 season to unlock significant revenue growth and strengthen Big Mountain Resort’s market position.**
+
+---
+
+## Appendix: Technical Details
+
+The following code and visualizations were used in the analysis. For further technical review, please refer to the project notebooks.
+
+```python
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Load the raw data
+df = pd.read_csv('../raw_data/ski_resort_data.csv')
+df.head()
+
+# Price by State Visualization
+plt.figure(figsize=(10,6))
+sns.boxplot(x='State', y='Price', data=df)
+plt.title('Ski Resort Prices by State')
+plt.show()
+
+# Amenities Distribution
+df['Amenity Count'] = df['Amenities'].apply(lambda x: len(str(x).split(',')))
+df['Amenity Count'].hist(bins=10)
+plt.title('Distribution of Number of Amenities')
+plt.xlabel('Number of Amenities')
+plt.ylabel('Number of Resorts')
+plt.show()
+
+# Missing Values Check
+df.isnull().sum()
+
+# Distribution of Resort Prices
+plt.hist(df['Price'], bins=20)
+plt.title('Distribution of Resort Prices')
+plt.xlabel('Price')
+plt.ylabel('Number of Resorts')
+plt.show()
+
+# Correlation Heatmap
+plt.figure(figsize=(8,6))
+sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')
+plt.title('Feature Correlation Heatmap')
+plt.show()
+
+# Random Forest Model Example
+from sklearn.ensemble import RandomForestRegressor
+# ...data preprocessing...
+model = RandomForestRegressor()
+model.fit(X_train, y_train)
+importances = model.feature_importances_
+
+# Feature Importance Plot
+features = X_train.columns
+feat_imp = pd.Series(importances, index=features).sort_values(ascending=False)
+feat_imp.plot(kind='bar', title='Feature Importances')
+plt.ylabel('Importance')
+plt.show()
+```
+
+---