california-housing-api

{"output": "total 24\ndrwxr-xr-x 1 root root 4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root 4096 Apr 22 21:02 ..\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 api\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 data\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 models\ndrwxr-xr-x 2 root root 4096 Mar  4 09:21 output", "exit_code": 0, "error": null}

I'll help you build the complete California housing price prediction pipeline. Let me start by exploring the environment and then proceed step by step.

→ terminal({"command": "cd /app && ls -la"})

{"output": "total 736\ndrwxr-xr-x 2 root root   4096 Mar  4 09:21 .\ndrwxr-xr-x 1 root root   4096 Mar  4 09:21 ..\n-rw-rw-r-- 1 root root 139441 Mar  4 09:05 test.zip\n-rw-rw-r-- 1 root root 601889 Mar  4 09:05 train.zip", "exit_code": 0, "error": null}

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

{"output": "Python 3.13.1", "exit_code": 0, "error": null}

{"output": "", "exit_code": 0, "error": null}

Let me start with Step 1: Unzipping the data files and loading the data.

→ terminal({"command": "cd /app/data && unzip -q train.zip && unzip -q test.zip"})

{"output": "total 2572\ndrwxr-xr-x 1 root root    4096 Apr 22 21:02 .\ndrwxr-xr-x 1 root root    4096 Mar  4 09:21 ..\n-rwxrwxrwx 1 root root  354101 Dec 22 08:24 test.csv\n-rw-rw-r-- 1 root root  139441 Mar  4 09:05 test.zip\n-rwxrwxrwx 1 root root 1516656 Dec 22 08:24 train.csv\n-rw-rw-r-- 1 root root  601889 Mar  4 09:05 train.zip", "exit_code": 0, "error": null}

{"content": "     1|MedInc,HouseAge,AveRooms,AveBedrms,Population,AveOccup,Latitude,Longitude,target\n     2|3.2596,33.0,5.017656500802568,1.0064205457463884,2300.0,3.691813804173355,32.71,-117.03,1.03\n     3|3.8125,49.0,4.473544973544974,1.041005291005291,1314.0,1.7380952380952381,33.77,-118.16,3.821\n     4|4.1563,4.0,5.645833333333333,0.9851190476190477,915.0,2.7232142857142856,34.66,-120.48,1.726\n     5|1.9425,36.0,4.002816901408451,1.0338028169014084,1418.0,3.9943661971830986,32.69,-117.11,0.934\n     6|", "total_lines": 16513, "file_size": 1516656, "truncated": true, "hint": "Use offset=6 to continue reading (showing 1-5 of 16513 lines)", "is_binary": false, "is_image": false}

{"bytes_written": 1614, "dirs_created": true}

Now let me create the data loading and preprocessing script:

→ write_file({"path": "/app/step1_data_prep.py", "content": "import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nimport json\nimport os\n\n# Step 1.1: Data files already unzipped\n\n# Step 1.2: Load training data\nprint(\"Loading training data...\")\ntrain_df = pd.read_csv('/app/data/train.csv')\nprint(f\"Loaded {len(train_df)} samples with {len(train_df.columns)} col…)

{"output": "Loading training data...\nLoaded 16512 samples with 9 columns\nColumns: ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude', 'target']\n\nSplitting data...\nTrain size: 13209\nValidation size: 3303\n\nSplit information saved to /app/output/split_info.json\nData preprocessing complete!", "exit_code": 0, "error": null}

{"content": "     1|{\n     2|  \"train_size\": 13209,\n     3|  \"validation_size\": 3303,\n     4|  \"random_state\": 42,\n     5|  \"test_size\": 0.2,\n     6|  \"train_indices\": [\n     7|    1,\n     8|    2,\n     9|    4,\n    10|    5,\n    11|    6,\n    12|    7,\n    13|    9,\n    14|    10,\n    15|    11,\n    16|    12,\n    17|    13,\n    18|    16,\n    19|    17,\n    20|    18,\n    21|    20,\n    22|    21,\n    23|    22,\n    24|    23,\n    25|    24,\n    26|    25,\n    27|    26,\n    28|    28,\n    29|    29,\n    30|    30,\n    31|    32,\n    32|    33,\n    33|    34,\n    34|    36,\n    35|    37,\n    36|    38,\n    37|    39,\n    38|    40,\n    39|    42,\n    40|    43,\n    41|    44,\n    42|    45,\n    43|    48,\n    44|    49,\n    45|    51,\n    46|    52,\n    47|    53,\n    48|    54,\n    49|    55,\n    50|    56,\n    51|    57,\n    52|    58,\n    53|    60,\n    54|    62,\n    55|    64,\n    56|    65,\n    57|    66,\n    58|    67,\n    59|    68,\n    60|    69,\n    61|    71,\n    62|    72,\n    63|    73,\n    64|    74,\n    65|    75,\n    66|    76,\n    67|    77,\n    68|    78,\n    69|    80,\n    70|    81,\n    71|    82,\n    72|    86,\n    73|    87,\n    74|    89,\n    75|    90,\n    76|    91,\n    77|    92,\n    78|    93,\n    79|    94,\n    80|    95,\n    81|    96,\n    82|    97,\n    83|    98,\n    84|    100,\n    85|    103,\n    86|    104,\n    87|    105,\n    88|    106,\n    89|    107,\n    90|    108,\n    91|    109,\n    92|    110,\n    93|    111,\n    94|    112,\n    95|    114,\n    96|    115,\n    97|    116,\n    98|    117,\n    99|    118,\n   100|    120,\n   101|    122,\n   102|    124,\n   103|    125,\n   104|    126,\n   105|    128,\n   106|    129,\n   107|    130,\n   108|    131,\n   109|    132,\n   110|    133,\n   111|    134,\n   112|    136,\n   113|    137,\n   114|    138,\n   115|    139,\n   116|    140,\n   117|    141,\n   118|    142,\n   119|    143,\n   120|    144,\n   121|    145,\n   122|    146,\n   123|    147,\n   124|    148,\n   125|    151,\n   126|    152,\n   127|    153,\n   128|    154,\n   129|    155,\n   130|    156,\n   131|    158,\n   132|    159,\n   133|    160,\n   134|    161,\n   135|    162,\n   136|    163,\n   137|    164,\n   138|    165,\n   139|    166,\n   140|    171,\n   141|    172,\n   142|    174,\n   143|    175,\n   144|    178,\n   145|    179,\n   146|    180,\n   147|    182,\n   148|    183,\n   149|    185,\n   150|    186,\n   151|    187,\n   152|    189,\n   153|    190,\n   154|    191,\n   155|    192,\n   156|    193,\n   157|    194,\n   158|    195,\n   159|    197,\n   160|    198,\n   161|    199,\n   162|    201,\n   163|    202,\n   164|    203,\n   165|    204,\n   166|    205,\n   167|    206,\n   168|    207,\n   169|    208,\n   170|    209,\n   171|    210,\n   172|    211,\n   173|    212,\n   174|    213,\n   175|    215,\n   176|    216,\n   177|    219,\n   178|    220,\n   179|    221,\n   180|    222,\n   181|    223,\n   182|    224,\n   183|    225,\n   184|    226,\n   185|    227,\n   186|    228,\n   187|    229,\n   188|    230,\n   189|    231,\n   190|    232,\n   191|    233,\n   192|    234,\n   193|    235,\n   194|    236,\n   195|    237,\n   196|    238,\n   197|    239,\n   198|    240,\n   199|    241,\n   200|    242,\n   201|    243,\n   202|    244,\n   203|    245,\n   204|    246,\n   205|    249,\n   206|    250,\n   207|    252,\n   208|    253,\n   209|    255,\n   210|    256,\n   211|    258,\n   212|    260,\n   213|    261,\n   214|    262,\n   215|    263,\n   216|    264,\n   217|    265,\n   218|    266,\n   219|    267,\n   220|    268,\n   221|    269,\n   222|    270,\n   223|    271,\n   224|    272,\n   225|    276,\n   226|    277,\n   227|    278,\n   228|    279,\n   229|    281,\n   230|    282,\n   231|    283,\n   232|    285,\n   233|    287,\n   234|    288,\n   235|    289,\n   23

{"bytes_written": 4415, "dirs_created": true}

Perfect! Now let me create the model training script for Step 2:

→ write_file({"path": "/app/step2_train_models.py", "content": "import pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression, Ridge\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\nimport json\nimport os\nimport pickle\n\n# Step 2.1: Load data\nprint(\"Loading data...\")\nX_train = pd.read_csv('/app…)

{"output": "/usr/local/lib/python3.13/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n  return fit_method(estimator, *args, **kwargs)\nLoading data...\nX_train shape: (13209, 8)\nX_val shape: (3303, 8)\ny_train shape: (13209, 1)\ny_val shape: (3303, 1)\n\nTraining Linear Regression...\nLinear Regression - Train R²: 0.6131, Val R²: 0.6097, MAE: 0.5407, RMSE: 0.7339\n\nTraining Ridge Regression...\nRidge Regression - Train R²: 0.6131, Val R²: 0.6097, MAE: 0.5407, RMSE: 0.7339\n\nTraining Random Forest Regressor...\nRandom Forest - Train R²: 0.9732, Val R²: 0.7985, MAE: 0.3459, RMSE: 0.5273\n\nBest model: random_forest with validation R²: 0.7985\n\nAll models saved to /app/models/\nModel comparison saved to /app/output/model_comparison.json\n\nValidation quality check:\nBest model validation R²: 0.7985\nBest model validation MAE: 0.3459\n✓ Validation quality requirements met!", "exit_code": 0, "error": null}

{"content": "     1|{\n     2|  \"models\": [\n     3|    {\n     4|      \"model_name\": \"linear_regression\",\n     5|      \"train_r2\": 0.6131488911003316,\n     6|      \"val_r2\": 0.6097023914123513,\n     7|      \"mae\": 0.5407140746076833,\n     8|      \"mse\": 0.5386059660291211,\n     9|      \"rmse\": 0.733897789906143\n    10|    },\n    11|    {\n    12|      \"model_name\": \"ridge\",\n    13|      \"train_r2\": 0.6131487849169934,\n    14|      \"val_r2\": 0.6096997448135448,\n    15|      \"mae\": 0.5407313629383259,\n    16|      \"mse\": 0.538609618303374,\n    17|      \"rmse\": 0.7339002781736589\n    18|    },\n    19|    {\n    20|      \"model_name\": \"random_forest\",\n    21|      \"train_r2\": 0.9732129585612862,\n    22|      \"val_r2\": 0.7984855340683008,\n    23|      \"mae\": 0.3458606364819863,\n    24|      \"mse\": 0.278087518867314,\n    25|      \"rmse\": 0.5273400410241138\n    26|    }\n    27|  ],\n    28|  \"best_model_name\": \"random_forest\"\n    29|}", "total_lines": 28, "file_size": 720, "truncated": false, "is_binary": false, "is_image": false}

{"output": "total 226596\ndrwxr-xr-x 1 root root      4096 Apr 22 21:08 .\ndrwxr-xr-x 1 root root      4096 Apr 22 21:08 ..\n-rw-r--r-- 1 root root 116002615 Apr 22 21:08 best_model.pkl\n-rw-r--r-- 1 root root       719 Apr 22 21:08 linear_regression.pkl\n-rw-r--r-- 1 root root 116002615 Apr 22 21:08 random_forest.pkl\n-rw-r--r-- 1 root root       689 Apr 22 21:08 ridge.pkl", "exit_code": 0, "error": null}

{"bytes_written": 2197, "dirs_created": true}

Great! Now let me create the FastAPI application for Step 4:

→ write_file({"path": "/app/api/app.py", "content": "from fastapi import FastAPI\nfrom pydantic import BaseModel\nimport pickle\nimport numpy as np\nfrom typing import List\nimport os\n\napp = FastAPI()\n\n# Model loading\nmodel = None\nmodel_name = \"random_forest\"\n\nclass PredictionRequest(BaseModel):\n    features: List[float]\n\nclass BatchPredictionRequest(BaseModel):\n    features_list: List[List[float…)

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

Now let me start the FastAPI server and test it:

→ terminal({"command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --reload"})

{"output": "Background process started", "session_id": "proc_5584f0293270", "pid": null, "exit_code": 0, "error": null}

{"status": "exited", "exit_code": -1, "output": ""}

{"session_id": "proc_5584f0293270", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

{"output": "API module loaded successfully", "exit_code": 0, "error": null}

Let me check if there's an issue with the server startup:

→ terminal({"command": "cd /app && python -c \"import api.app; print('API module loaded successfully')\""})

{"output": "Model loaded successfully", "exit_code": 0, "error": null}

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

Let me try starting the server again with a simple test:

→ terminal({"command": "cd /app && timeout 5 uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1 | head -20"})

{"bytes_written": 750, "dirs_created": true}

Let me try a different approach and test the server startup:

→ write_file({"path": "/app/start_server.py", "content": "import subprocess\nimport time\nimport requests\nimport signal\nimport os\n\n# Start the server\nproc = subprocess.Popen(\n    [\"uvicorn\", \"api.app:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\"],\n    stdout=subprocess.PIPE,\n    stderr=subprocess.PIPE,\n    text=True\n)\n\nprint(\"Server started with PID:\", proc.pid)\n\n# Wait for server to …)

{"output": "Server started with PID: 547\nHealth check response: {'status': 'healthy', 'model_loaded': True}", "exit_code": 0, "error": null}

{"bytes_written": 1791, "dirs_created": true}

Excellent! The server works. Now let me test the API endpoints and then proceed with Step 6 to test the API with the validation set:

→ write_file({"path": "/app/test_api.py", "content": "import subprocess\nimport time\nimport requests\nimport pandas as pd\nimport json\n\n# Start server in background\nproc = subprocess.Popen(\n    [\"uvicorn\", \"api.app:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\"],\n    stdout=subprocess.PIPE,\n    stderr=subprocess.PIPE,\n    text=True\n)\n\nprint(\"Server started with PID:\", proc.pid)\n\n# Wait …)

{"output": "Server started with PID: 674\n\n=== Testing /health ===\nResponse: {'status': 'healthy', 'model_loaded': True}\n\n=== Testing /model-info ===\nResponse: {'model_name': 'random_forest', 'model_type': 'RandomForestRegressor', 'features_count': 8}\n\n=== Testing /predict ===\nResponse: {'prediction': 1.3311700000000002, 'model_name': 'random_forest'}\n\n=== Testing /predict-batch ===\nResponse: {'predictions': [1.3311700000000002, 3.774750500000004], 'model_name': 'random_forest', 'count': 2}\n\n✓ All API endpoints working correctly!", "exit_code": 0, "error": null}

{"bytes_written": 2090, "dirs_created": true}

Perfect! Now let me create the script for Step 6 to test the API with the validation set:

→ write_file({"path": "/app/step6_test_api.py", "content": "import subprocess\nimport time\nimport requests\nimport pandas as pd\nimport json\nimport numpy as np\n\n# Start server in background\nproc = subprocess.Popen(\n    [\"uvicorn\", \"api.app:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\"],\n    stdout=subprocess.PIPE,\n    stderr=subprocess.PIPE,\n    text=True\n)\n\nprint(\"Server started with PI…)