Day-9-exercise-your-first-machine-learning-model.ipynb

{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**This notebook is an exercise in the [Introduction to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) course.  You can reference the tutorial at [this link](https://www.kaggle.com/dansbecker/your-first-machine-learning-model).**\n\n---\n","metadata":{}},{"cell_type":"markdown","source":"## Recap\nSo far, you have loaded your data and reviewed it with the following code. Run this cell to set up your coding environment where the previous step left off.","metadata":{}},{"cell_type":"code","source":"# Code you have previously used to load data\nimport pandas as pd\n\n# Path of the file to read\niowa_file_path = '../input/home-data-for-ml-course/train.csv'\n\nhome_data = pd.read_csv(iowa_file_path)\n\n# Set up code checking\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.machine_learning.ex3 import *\n\nprint(\"Setup Complete\")","metadata":{"execution":{"iopub.status.busy":"2021-08-13T08:02:17.370051Z","iopub.execute_input":"2021-08-13T08:02:17.370725Z","iopub.status.idle":"2021-08-13T08:02:18.804434Z","shell.execute_reply.started":"2021-08-13T08:02:17.370566Z","shell.execute_reply":"2021-08-13T08:02:18.803264Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Setup Complete\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Exercises\n\n## Step 1: Specify Prediction Target\nSelect the target variable, which corresponds to the sales price. Save this to a new variable called `y`. You'll need to print a list of the columns to find the name of the column you need.\n","metadata":{}},{"cell_type":"code","source":"# print the list of columns in the dataset to find the name of the prediction target\nhome_data.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-13T08:02:45.643813Z","iopub.execute_input":"2021-08-13T08:02:45.644239Z","iopub.status.idle":"2021-08-13T08:02:45.694110Z","shell.execute_reply.started":"2021-08-13T08:02:45.644201Z","shell.execute_reply":"2021-08-13T08:02:45.692828Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n\n  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n\n  YrSold  SaleType  SaleCondition  SalePrice  \n0   2008        WD         Normal     208500  \n1   2007        WD         Normal     181500  \n2   2008        WD         Normal     223500  \n3   2006        WD        Abnorml     140000  \n4   2008        WD         Normal     250000  \n\n[5 rows x 81 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>MSSubClass</th>\n      <th>MSZoning</th>\n      <th>LotFrontage</th>\n      <th>LotArea</th>\n      <th>Street</th>\n      <th>Alley</th>\n      <th>LotShape</th>\n      <th>LandContour</th>\n      <th>Utilities</th>\n      <th>...</th>\n      <th>PoolArea</th>\n      <th>PoolQC</th>\n      <th>Fence</th>\n      <th>MiscFeature</th>\n      <th>MiscVal</th>\n      <th>MoSold</th>\n      <th>YrSold</th>\n      <th>SaleType</th>\n      <th>SaleCondition</th>\n      <th>SalePrice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>65.0</td>\n      <td>8450</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>208500</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>20</td>\n      <td>RL</td>\n      <td>80.0</td>\n      <td>9600</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>5</td>\n      <td>2007</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>181500</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>68.0</td>\n      <td>11250</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>223500</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>70</td>\n      <td>RL</td>\n      <td>60.0</td>\n      <td>9550</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2006</td>\n      <td>WD</td>\n      <td>Abnorml</td>\n      <td>140000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>84.0</td>\n      <td>14260</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>12</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>250000</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 81 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y = home_data.SalePrice\n\n# Check your answer\nstep_1.check()","metadata":{"execution":{"iopub.status.busy":"2021-08-13T08:03:12.544848Z","iopub.execute_input":"2021-08-13T08:03:12.545346Z","iopub.status.idle":"2021-08-13T08:03:12.556972Z","shell.execute_reply.started":"2021-08-13T08:03:12.545313Z","shell.execute_reply":"2021-08-13T08:03:12.555771Z"},"trusted":true},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"1_SetTarget\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}}]},{"cell_type":"code","source":"# The lines below will show you a hint or the solution.\n# step_1.hint() \n# step_1.solution()","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Step 2: Create X\nNow you will create a DataFrame called `X` holding the predictive features.\n\nSince you want only some columns from the original data, you'll first create a list with the names of the columns you want in `X`.\n\nYou'll use just the following columns in the list (you can copy and paste the whole list to save some typing, though you'll still need to add quotes):\n    * LotArea\n    * YearBuilt\n    * 1stFlrSF\n    * 2ndFlrSF\n    * FullBath\n    * BedroomAbvGr\n    * TotRmsAbvGrd\n\nAfter you've created that list of features, use it to create the DataFrame that you'll use to fit the model.","metadata":{}},{"cell_type":"code","source":"# Create the list of features below\nfeature_names = [\"LotArea\",\"YearBuilt\",\"1stFlrSF\", \"2ndFlrSF\",\"FullBath\", \"BedroomAbvGr\",\"TotRmsAbvGrd\"]\n\n# Select data corresponding to features in feature_names\nX = home_data[feature_names]\n\n# Check your answer\nstep_2.check()","metadata":{"execution":{"iopub.status.busy":"2021-08-13T08:14:54.613407Z","iopub.execute_input":"2021-08-13T08:14:54.614049Z","iopub.status.idle":"2021-08-13T08:14:54.625830Z","shell.execute_reply.started":"2021-08-13T08:14:54.614013Z","shell.execute_reply":"2021-08-13T08:14:54.624511Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"2_SelectPredictionData\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}}]},{"cell_type":"code","source":"# step_2.hint()\n#step_2.solution()","metadata":{"execution":{"iopub.status.busy":"2021-08-13T08:14:20.317261Z","iopub.execute_input":"2021-08-13T08:14:20.317850Z","iopub.status.idle":"2021-08-13T08:14:20.320791Z","shell.execute_reply.started":"2021-08-13T08:14:20.317813Z","shell.execute_reply":"2021-08-13T08:14:20.320031Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":"## Review Data\nBefore building a model, take a quick look at **X** to verify it looks sensible","metadata":{}},{"cell_type":"code","source":"# Review data\n# print description or statistics from X\nprint(X.describe())\n\n# print the top few lines\nprint(X.head())","metadata":{"execution":{"iopub.status.busy":"2021-08-13T08:15:39.977528Z","iopub.execute_input":"2021-08-13T08:15:39.978070Z","iopub.status.idle":"2021-08-13T08:15:40.006977Z","shell.execute_reply.started":"2021-08-13T08:15:39.978020Z","shell.execute_reply":"2021-08-13T08:15:40.005729Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"             LotArea    YearBuilt     1stFlrSF     2ndFlrSF     FullBath  \\\ncount    1460.000000  1460.000000  1460.000000  1460.000000  1460.000000   \nmean    10516.828082  1971.267808  1162.626712   346.992466     1.565068   \nstd      9981.264932    30.202904   386.587738   436.528436     0.550916   \nmin      1300.000000  1872.000000   334.000000     0.000000     0.000000   \n25%      7553.500000  1954.000000   882.000000     0.000000     1.000000   \n50%      9478.500000  1973.000000  1087.000000     0.000000     2.000000   \n75%     11601.500000  2000.000000  1391.250000   728.000000     2.000000   \nmax    215245.000000  2010.000000  4692.000000  2065.000000     3.000000   \n\n       BedroomAbvGr  TotRmsAbvGrd  \ncount   1460.000000   1460.000000  \nmean       2.866438      6.517808  \nstd        0.815778      1.625393  \nmin        0.000000      2.000000  \n25%        2.000000      5.000000  \n50%        3.000000      6.000000  \n75%        3.000000      7.000000  \nmax        8.000000     14.000000  \n   LotArea  YearBuilt  1stFlrSF  2ndFlrSF  FullBath  BedroomAbvGr  \\\n0     8450       2003       856       854         2             3   \n1     9600       1976      1262         0         2             3   \n2    11250       2001       920       866         2             3   \n3     9550       1915       961       756         1             3   \n4    14260       2000      1145      1053         2             4   \n\n   TotRmsAbvGrd  \n0             8  \n1             6  \n2             6  \n3             7  \n4             9  \n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Step 3: Specify and Fit Model\nCreate a `DecisionTreeRegressor` and save it iowa_model. Ensure you've done the relevant import from sklearn to run this command.\n\nThen fit the model you just created using the data in `X` and `y` that you saved above.","metadata":{}},{"cell_type":"code","source":"from sklearn.tree import DecisionTreeRegressor\n#specify the model. \n#For model reproducibility, set a numeric value for random_state when specifying the model\niowa_model = DecisionTreeRegressor(random_state=0)\n\n# Fit the model\niowa_model.fit(X,y)\n\n# Check your answer\nstep_3.check()","metadata":{"execution":{"iopub.status.busy":"2021-08-13T08:17:40.803051Z","iopub.execute_input":"2021-08-13T08:17:40.803448Z","iopub.status.idle":"2021-08-13T08:17:40.827109Z","shell.execute_reply.started":"2021-08-13T08:17:40.803416Z","shell.execute_reply":"2021-08-13T08:17:40.826055Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3_CreateModel\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}}]},{"cell_type":"code","source":"# step_3.hint()\n# step_3.solution()","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Step 4: Make Predictions\nMake predictions with the model's `predict` command using `X` as the data. Save the results to a variable called `predictions`.","metadata":{}},{"cell_type":"code","source":"predictions =  iowa_model.predict(X, check_input=True)\nprint(predictions)\n\n# Check your answer\nstep_4.check()","metadata":{"execution":{"iopub.status.busy":"2021-08-13T08:19:02.270113Z","iopub.execute_input":"2021-08-13T08:19:02.270534Z","iopub.status.idle":"2021-08-13T08:19:02.284369Z","shell.execute_reply.started":"2021-08-13T08:19:02.270498Z","shell.execute_reply":"2021-08-13T08:19:02.283580Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"[208500. 181500. 223500. ... 266500. 142125. 147500.]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"4_MakePredictions\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}}]},{"cell_type":"code","source":"# step_4.hint()\n# step_4.solution()","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Think About Your Results\n\nUse the `head` method to compare the top few predictions to the actual home values (in `y`) for those same homes. Anything surprising?\n","metadata":{}},{"cell_type":"code","source":"# You can write code in this cell\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It's natural to ask how accurate the model's predictions will be and how you can improve that. That will be you're next step.\n\n# Keep Going\n\nYou are ready for **[Model Validation](https://www.kaggle.com/dansbecker/model-validation).**\n","metadata":{}},{"cell_type":"markdown","source":"---\n\n\n\n\n*Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum/161285) to chat with other Learners.*","metadata":{}}]}