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

{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.10","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":"2023-06-07T05:26:50.994566Z","iopub.execute_input":"2023-06-07T05:26:50.994983Z","iopub.status.idle":"2023-06-07T05:26:53.025698Z","shell.execute_reply.started":"2023-06-07T05:26:50.994947Z","shell.execute_reply":"2023-06-07T05:26:53.024778Z"},"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":"\n# print the list of columns in the dataset to find the name of the prediction target\nhome_data.columns\n","metadata":{"execution":{"iopub.status.busy":"2023-06-07T05:27:13.488106Z","iopub.execute_input":"2023-06-07T05:27:13.488509Z","iopub.status.idle":"2023-06-07T05:27:13.498364Z","shell.execute_reply.started":"2023-06-07T05:27:13.488474Z","shell.execute_reply":"2023-06-07T05:27:13.496795Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n       'SaleCondition', 'SalePrice'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"y = home_data.SalePrice\n\n# Check your answer\nstep_1.check()","metadata":{"execution":{"iopub.status.busy":"2023-06-07T05:27:25.340920Z","iopub.execute_input":"2023-06-07T05:27:25.341651Z","iopub.status.idle":"2023-06-07T05:27:25.351633Z","shell.execute_reply.started":"2023-06-07T05:27:25.341609Z","shell.execute_reply":"2023-06-07T05:27:25.350313Z"},"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":"\n# 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()\n# 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":"2023-06-07T05:27:38.698943Z","iopub.execute_input":"2023-06-07T05:27:38.699332Z","iopub.status.idle":"2023-06-07T05:27:38.723449Z","shell.execute_reply.started":"2023-06-07T05:27:38.699299Z","shell.execute_reply":"2023-06-07T05:27:38.722326Z"},"trusted":true},"execution_count":5,"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":{}},{"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":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":null,"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":"2023-06-07T05:27:53.493317Z","iopub.execute_input":"2023-06-07T05:27:53.493707Z","iopub.status.idle":"2023-06-07T05:27:53.533287Z","shell.execute_reply.started":"2023-06-07T05:27:53.493674Z","shell.execute_reply":"2023-06-07T05:27:53.532118Z"},"trusted":true},"execution_count":6,"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\n#specify the model. \n#For model reproducibility, set a numeric value for random_state when specifying the model\niowa_model = DecisionTreeRegressor(random_state=1)\n\n# Fit the model\niowa_model.fit(X,y)\n\n# Check your answer\nstep_3.check()","metadata":{"execution":{"iopub.status.busy":"2023-06-07T05:28:08.674177Z","iopub.execute_input":"2023-06-07T05:28:08.674544Z","iopub.status.idle":"2023-06-07T05:28:08.696318Z","shell.execute_reply.started":"2023-06-07T05:28:08.674514Z","shell.execute_reply":"2023-06-07T05:28:08.695248Z"},"trusted":true},"execution_count":7,"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 = ____\nprint(predictions)\n\n# Check your answer\nstep_4.check()","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"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 [course discussion forum](https://www.kaggle.com/learn/intro-to-machine-learning/discussion) to chat with other learners.*","metadata":{}}]}