{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":112899,"databundleVersionId":13449579,"sourceType":"competition"},{"sourceId":12930004,"sourceType":"datasetVersion","datasetId":8181955}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Grand X-Ray Slam Division A - Week 2 Progress Report\n\n*Competition Progress Analysis • Week 2 of 8 • September 2025*\n\n---\n\n## Competition Overview\n\n**Grand X-Ray Slam Division A** is heating up as we enter the second week of this prestigious medical imaging competition. Teams are pushing the boundaries of AI-driven X-ray analysis with impressive results.\n\n| Metric | Value |\n|--------|-------|\n| **Total Entrants** | 217 participants |\n| **Active Teams** | 34 teams competing |\n| **Submissions** | 99 total submissions |\n| **Competition Period** | 8 weeks (Started 11 days ago) |\n| **Deadline** | October 15, 2025 |\n\n---","metadata":{}},{"cell_type":"markdown","source":"## Setup & Data Loading","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport warnings\nwarnings.filterwarnings('ignore')\n\n!pip install --upgrade plotly\n\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\nfrom datetime import datetime, timedelta\n\nplt.style.use('seaborn-v0_8-darkgrid')\nsns.set_palette(\"husl\")\n\nprint(\"Libraries loaded successfully!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-01T16:08:23.40716Z","iopub.execute_input":"2025-09-01T16:08:23.407502Z","iopub.status.idle":"2025-09-01T16:08:47.152815Z","shell.execute_reply.started":"2025-09-01T16:08:23.407474Z","shell.execute_reply":"2025-09-01T16:08:47.151915Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/division-a-leaderboard/leaderboard.csv')\n\nprint(f\"Leaderboard loaded: {len(df)} teams\")\nprint(f\"Data shape: {df.shape}\")\n\nprint(\"\\n TOP 5 TEAMS:\")\nprint(\"=\" * 50)\ntop_5 = df.head()\nfor _, row in top_5.iterrows():\n    print(f\"{row['Rank']:2d}. {row['TeamName'][:20]:<20} | Score: {row['Score']:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-01T16:08:47.154619Z","iopub.execute_input":"2025-09-01T16:08:47.155262Z","iopub.status.idle":"2025-09-01T16:08:47.181702Z","shell.execute_reply.started":"2025-09-01T16:08:47.155238Z","shell.execute_reply":"2025-09-01T16:08:47.180975Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Current Leaderboard Standings","metadata":{}},{"cell_type":"code","source":"df['LastSubmissionDate'] = pd.to_datetime(df['LastSubmissionDate'])\ndf['DaysAgo'] = (datetime.now() - df['LastSubmissionDate']).dt.days\n\nfig = make_subplots(\n    rows=1, cols=2,\n    subplot_titles=('Top 15 Teams Performance', 'Score Distribution'),\n    specs=[[{'type': 'bar'}, {'type': 'histogram'}]]\n)\n\ntop_15 = df.head(15)\nfig.add_trace(\n    go.Bar(\n        x=top_15['Score'],\n        y=top_15['TeamName'],\n        orientation='h',\n        marker_color='lightblue',\n        text=[f\"#{rank}\" for rank in top_15['Rank']],\n        textposition='inside'\n    ),\n    row=1, col=1\n)\n\nfig.add_trace(\n    go.Histogram(\n        x=df['Score'],\n        nbinsx=20,\n        marker_color='lightcoral',\n        opacity=0.7\n    ),\n    row=1, col=2\n)\n\nfig.update_layout(\n    height=600,\n    title_text=\" Grand X-Ray Slam Division A - Current Standings\",\n    showlegend=False\n)\n\nfig.update_xaxes(title_text=\"Score\", row=1, col=1)\nfig.update_yaxes(title_text=\"Team\", row=1, col=1)\nfig.update_xaxes(title_text=\"Score\", row=1, col=2)\nfig.update_yaxes(title_text=\"Frequency\", row=1, col=2)\n\nfig.show()\n\nprint(f\"\\n PERFORMANCE METRICS:\")\nprint(f\" Leading Score: {df['Score'].max():.6f}\")\nprint(f\" Average Score: {df['Score'].mean():.6f}\")\nprint(f\" Score Range: {df['Score'].min():.6f} - {df['Score'].max():.6f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-01T16:08:47.182473Z","iopub.execute_input":"2025-09-01T16:08:47.182761Z","iopub.status.idle":"2025-09-01T16:08:47.664738Z","shell.execute_reply.started":"2025-09-01T16:08:47.182731Z","shell.execute_reply":"2025-09-01T16:08:47.663852Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Submission Activity Analysis","metadata":{}},{"cell_type":"code","source":"fig = plt.figure(figsize=(16,10))\nfig.suptitle(' Competition Activity Insights', fontsize=16, fontweight='bold')\n\nax1 = fig.add_subplot(2,2,1)\nax1.hist(df['SubmissionCount'], bins=15, alpha=0.7, color='skyblue', edgecolor='black')\nax1.set_title('Submissions per Team')\nax1.set_xlabel('Number of Submissions')\nax1.set_ylabel('Teams')\nax1.grid(True, alpha=0.3)\n\nax2 = fig.add_subplot(2,2,2)\ndf['ActivityLevel'] = df['SubmissionCount'].apply(\n    lambda x: 'High (3+)' if x >= 3 else 'Medium (2)' if x == 2 else 'Low (1)'\n)\nactivity_counts = df['ActivityLevel'].value_counts()\nax2.pie(activity_counts.values, labels=activity_counts.index, autopct='%1.1f%%', \n        colors=['lightgreen', 'orange', 'lightcoral'])\nax2.set_title('Team Activity Levels')\n\nax3 = fig.add_subplot(2,1,2)\nax3.scatter(df['SubmissionCount'], df['Score'], alpha=0.6, color='purple', s=80)\nax3.set_xlabel('Number of Submissions')\nax3.set_ylabel('Score')\nax3.set_title('Score vs Submission Count')\nax3.grid(True, alpha=0.3)\n\nz = np.polyfit(df['SubmissionCount'], df['Score'], 1)\np = np.poly1d(z)\nax3.plot(df['SubmissionCount'], p(df['SubmissionCount']), \"r--\", alpha=0.8)\n\nplt.tight_layout()\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-01T16:08:47.667207Z","iopub.execute_input":"2025-09-01T16:08:47.667444Z","iopub.status.idle":"2025-09-01T16:08:48.346271Z","shell.execute_reply.started":"2025-09-01T16:08:47.667426Z","shell.execute_reply":"2025-09-01T16:08:48.345431Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Competition Highlights","metadata":{}},{"cell_type":"code","source":"recent_submissions = df[df['DaysAgo'] <= 2]\nactive_teams = len(recent_submissions)\n\nprint(\" WEEK 2 HIGHLIGHTS\")\nprint(\"=\" * 40)\nprint(f\" {active_teams} teams submitted in last 48h\")\nprint(f\" Leading team: {df.iloc[0]['TeamName']}\")\nprint(f\" Current best score: {df.iloc[0]['Score']:.6f}\")\nprint(f\" Most active team: {df.loc[df['SubmissionCount'].idxmax(), 'TeamName']} ({df['SubmissionCount'].max()} submissions)\")\n\nintensity_score = (df['SubmissionCount'].sum() / len(df)) * (active_teams / len(df))\nprint(f\" Competition Intensity: {intensity_score:.2f}/1.0\")\n\nfig = go.Figure()\n\nfig.add_trace(go.Scatter(\n    x=list(range(1, len(df)+1)),\n    y=df['Score'],\n    mode='markers+lines',\n    marker=dict(\n        size=df['SubmissionCount']*3,\n        color=df['SubmissionCount'],\n        colorscale='viridis',\n        showscale=True,\n        colorbar=dict(title=\"Submissions\")\n    ),\n    line=dict(width=1, color='lightgray'),\n    text=df['TeamName'],\n    hovertemplate='<b>%{text}</b><br>Rank: %{x}<br>Score: %{y:.6f}<extra></extra>'\n))\n\nfig.update_layout(\n    title=' Team Performance Landscape (Bubble size = Submissions)',\n    xaxis_title='Team Rank',\n    yaxis_title='Score',\n    height=500,\n    hovermode='closest'\n)\n\nfig.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-01T16:08:48.347401Z","iopub.execute_input":"2025-09-01T16:08:48.347886Z","iopub.status.idle":"2025-09-01T16:08:48.423598Z","shell.execute_reply.started":"2025-09-01T16:08:48.347854Z","shell.execute_reply":"2025-09-01T16:08:48.422729Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## Timeline","metadata":{}},{"cell_type":"code","source":"timeline_data = {\n    'Phase': ['Competition Start', 'Week 1 Complete', 'Current (Week 2)', 'Final Submission', 'Results'],\n    'Date': ['Aug 21', 'Aug 28', 'Sep 1', 'Oct 15', 'Oct 17'],\n    'Status': ['Complete', 'Complete', 'Active', 'Upcoming', 'Upcoming']\n}\ntimeline_df = pd.DataFrame(timeline_data)\n\nfig = go.Figure()\ncolors = {'Complete': 'green', 'Active': 'blue', 'Upcoming': 'gray'}\n\nfor status in timeline_df['Status'].unique():\n    mask = timeline_df['Status'] == status\n    fig.add_trace(go.Scatter(\n        x=timeline_df[mask]['Date'],\n        y=timeline_df[mask]['Phase'],\n        mode='markers',\n        marker=dict(size=15, color=colors[status]),\n        name=status,\n        text=timeline_df[mask]['Status'],\n        textposition='middle right'\n    ))\n\nfig.update_layout(\n    title=' Competition Timeline',\n    xaxis_title='Date',\n    height=300,\n    showlegend=True\n)\nfig.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-01T16:08:48.424549Z","iopub.execute_input":"2025-09-01T16:08:48.424886Z","iopub.status.idle":"2025-09-01T16:08:48.495283Z","shell.execute_reply.started":"2025-09-01T16:08:48.424858Z","shell.execute_reply":"2025-09-01T16:08:48.494328Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"total_days = 55\ndays_passed = 11\ndays_left = total_days - days_passed \nprogress_pct = (days_passed / total_days) * 100\n\nfig_progress = go.Figure(go.Bar(\n    x=[total_days],\n    y=[\"Competition Progress\"],\n    orientation=\"h\",\n    marker=dict(color=\"lightgray\"),\n    width=0.5,\n    showlegend=False\n))\n\nfig_progress.add_trace(go.Bar(\n    x=[days_passed],\n    y=[\"Competition Progress\"],\n    orientation=\"h\",\n    text=[f\"{progress_pct:.1f}% complete\"],\n    textposition=\"inside\",\n    marker=dict(color=\"green\"),\n    width=0.5,\n    name=\"Days Passed\"\n))\n\nfig_progress.update_layout(\n    title=\"Competition Progress (Day 1 → Day 55)\",\n    barmode='overlay',\n    xaxis=dict(range=[0, total_days], title=\"Days\"),\n    yaxis=dict(showticklabels=False),\n    height=200\n)\n\nfig_progress.show()\n\nprint(\" REMAINING TIME:\")\nprint(f\" Days until deadline: {days_left} days\")\nprint(f\" Competition progress: {progress_pct:.1f}% complete\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-09-01T16:08:48.496166Z","iopub.execute_input":"2025-09-01T16:08:48.496374Z","iopub.status.idle":"2025-09-01T16:08:48.55693Z","shell.execute_reply.started":"2025-09-01T16:08:48.496357Z","shell.execute_reply":"2025-09-01T16:08:48.556019Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n\n## What's Next?\n\n**Week 3 Expectations:**\n- **Score Push**: Expect teams to break the 0.93 barrier\n- **Submission Surge**: Anticipating 150+ total submissions\n- **New Strategies**: Teams likely to implement ensemble methods\n- **Final Sprint**: Last chance for major algorithmic improvements\n\n**Key Areas to Watch:**\n- Advanced data augmentation techniques\n- Ensemble model implementations  \n- Feature engineering innovations\n- Cross-validation strategy optimizations\n\n---","metadata":{}},{"cell_type":"markdown","source":"## Resources & Links\n\n**Competition Resources:**\n- [Competition Homepage](https://www.kaggle.com/competitions/grand-xray-slam-division-a) \n- [Discussion Forum](https://www.kaggle.com/competitions/grand-xray-slam-division-a/discussion?sort=hotness)\n- [Starter EDA Notebook](https://www.kaggle.com/code/guntasdhanjal/grand-x-ray-slam-division-a-eda)\n- [Baseline Model Notebook](https://www.kaggle.com/code/guntasdhanjal/simple-cnn-baseline-for-division-a)\n\n**Related Competitions:**\n- [Grand X-Ray Slam Division B](https://www.kaggle.com/competitions/grand-xray-slam-division-b) - Sister competition with different dataset\n\n---\n\n* This progress report will be updated weekly. Follow for the latest competition insights and analysis!*\n\n**Next Update:** Week 3 Progress Report (September 8, 2025)\n\n---\n\n**Good luck to all participants! May the best model win!**","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}