prediction image update

README.md

@@ -1,6 +1,6 @@
 # Tension Board 2: Predicting Climbing Route Difficulty from Board Data
 
-I recently got into *board climbing*, and have been enjoying using the <a href="https://tensionclimbing.com/products/tension-board-2">Tension Board 2</a>. I've been climbing on the 12ftx12ft (mirrored) that is available at my local gym, and I've never felt that the phrase "*it hurts so good*" would be so apt. As such, I decided to do an in depth analysis of available data. 
+I recently got into *board climbing*, and have been enjoying using the <a href="https://tensionclimbing.com/products/tension-board-2">Tension Board 2</a>. I've been climbing on the 12ftx12ft (mirrored) that is available at my local gym, and I've never felt that the phrase "*it hurts so good*" would be so apt. As such, I decided to do an in depth analysis of available data. See also an <a href="https://gitlab.com/psark/Kilter-Board-Analysis">analysis of the Kilter Board</a>.
 
 ![Hold Usage Heatmap](images/02_hold_stats/all_holds_all_grades_heatmap.png)
 
@@ -11,7 +11,7 @@ I recently got into *board climbing*, and have been enjoying using the <a href="
 
 ## Overview
 
-This project analyzes ~130,000 climbs from the Tension Boards in order to do the following.
+This project analyzes ~130,000 climbs on the Tension Boards in order to do the following.
 > 1. **Understand** hold usage patterns and difficulty distributions
 > 2. **Quantify** empircal hold difficulty scores
 > 3. **Predict** climb grades from spatial and structural features of climbs
@@ -91,7 +91,6 @@ Go to your working directory and run notebooks in order:
 
 Note:
 
-* Notebooks 01-03 are uploaded with all of their cells run, so that one can see the data analysis. Notebooks 04-06 are uploaded without having been run.
 * Notebook 03 generates global hold difficulty tables
 * Notebook 04 generates feature matrix
 * Notebook 05 trains models

notebooks/05_predictive_modelling.ipynb

@@ -367,6 +367,7 @@
     "    ax.set_title(f'{model_name}: Residual Plot', fontsize=14)\n",
     "\n",
     "    plt.tight_layout()\n",
+    "    plt.savefig(f'../images/05_predictive_modelling/{model_name.lower().replace(\" \", \"_\")}_predictions.png', dpi=150, bbox_inches='tight')\n",
     "    plt.show()\n",
     "\n",
     "\n",
@@ -398,6 +399,7 @@
     "    plt.suptitle('')  # Remove automatic title\n",
     "\n",
     "    plt.tight_layout()\n",
+    "    plt.savefig(f'../images/05_predictive_modelling/{model_name.lower().replace(\" \", \"_\")}_errors.png', dpi=150, bbox_inches='tight')\n",
     "    plt.show()\n"
    ]
   },

requirements.txt

@@ -6,5 +6,4 @@ scikit-learn
 jupyter
 notebook
 torch
-sqlite3
 boardlib