{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"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":"2022-07-24T08:51:54.882204Z","iopub.execute_input":"2022-07-24T08:51:54.882918Z","iopub.status.idle":"2022-07-24T08:51:56.484774Z","shell.execute_reply.started":"2022-07-24T08:51:54.882820Z","shell.execute_reply":"2022-07-24T08:51:56.483446Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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.columns","metadata":{"execution":{"iopub.status.busy":"2022-07-24T08:52:50.322781Z","iopub.execute_input":"2022-07-24T08:52:50.323212Z","iopub.status.idle":"2022-07-24T08:52:50.335125Z","shell.execute_reply.started":"2022-07-24T08:52:50.323178Z","shell.execute_reply":"2022-07-24T08:52:50.334126Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y = home_data.SalePrice\n\n# Check your answer\nstep_1.check()","metadata":{"execution":{"iopub.status.busy":"2022-07-24T08:53:11.902845Z","iopub.execute_input":"2022-07-24T08:53:11.903792Z","iopub.status.idle":"2022-07-24T08:53:11.919163Z","shell.execute_reply.started":"2022-07-24T08:53:11.903756Z","shell.execute_reply":"2022-07-24T08:53:11.917792Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# The lines below will show you a hint or the solution.\n# step_1.hint() \n# step_1.solution()","metadata":{},"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":"2022-07-24T08:53:32.688616Z","iopub.execute_input":"2022-07-24T08:53:32.689648Z","iopub.status.idle":"2022-07-24T08:53:32.706944Z","shell.execute_reply.started":"2022-07-24T08:53:32.689600Z","shell.execute_reply":"2022-07-24T08:53:32.706102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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":"2022-07-24T08:54:21.303069Z","iopub.execute_input":"2022-07-24T08:54:21.304064Z","iopub.status.idle":"2022-07-24T08:54:21.351466Z","shell.execute_reply.started":"2022-07-24T08:54:21.304008Z","shell.execute_reply":"2022-07-24T08:54:21.350169Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X.describe()","metadata":{"execution":{"iopub.status.busy":"2022-07-24T08:54:43.282721Z","iopub.execute_input":"2022-07-24T08:54:43.283430Z","iopub.status.idle":"2022-07-24T08:54:43.319238Z","shell.execute_reply.started":"2022-07-24T08:54:43.283392Z","shell.execute_reply":"2022-07-24T08:54:43.318449Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X.head()","metadata":{"execution":{"iopub.status.busy":"2022-07-24T08:54:47.021855Z","iopub.execute_input":"2022-07-24T08:54:47.022456Z","iopub.status.idle":"2022-07-24T08:54:47.034097Z","shell.execute_reply.started":"2022-07-24T08:54:47.022413Z","shell.execute_reply":"2022-07-24T08:54:47.033122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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=1)\n\n# Fit the model\niowa_model.fit(X,y)\n\n# Check your answer\nstep_3.check()","metadata":{"execution":{"iopub.status.busy":"2022-07-24T08:55:14.122575Z","iopub.execute_input":"2022-07-24T08:55:14.122956Z","iopub.status.idle":"2022-07-24T08:55:14.143889Z","shell.execute_reply.started":"2022-07-24T08:55:14.122916Z","shell.execute_reply":"2022-07-24T08:55:14.142816Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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":"# step_4.hint()\n# step_4.solution()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions = iowa_model.predict(X)\nprint(predictions)\n\n# Check your answer\nstep_4.check()","metadata":{"execution":{"iopub.status.busy":"2022-07-24T08:55:45.772581Z","iopub.execute_input":"2022-07-24T08:55:45.773029Z","iopub.status.idle":"2022-07-24T08:55:45.787235Z","shell.execute_reply.started":"2022-07-24T08:55:45.772980Z","shell.execute_reply":"2022-07-24T08:55:45.786323Z"},"trusted":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":"from sklearn.metrics import r2_score\nr2_score(y,predictions)","metadata":{"execution":{"iopub.status.busy":"2022-07-24T09:05:37.078812Z","iopub.execute_input":"2022-07-24T09:05:37.079441Z","iopub.status.idle":"2022-07-24T09:05:37.087826Z","shell.execute_reply.started":"2022-07-24T09:05:37.079402Z","shell.execute_reply":"2022-07-24T09:05:37.086588Z"},"trusted":true},"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":{}}]}