{"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":"2022-07-27T08:43:53.319606Z","iopub.execute_input":"2022-07-27T08:43:53.320086Z","iopub.status.idle":"2022-07-27T08:43:54.229384Z","shell.execute_reply.started":"2022-07-27T08:43:53.319994Z","shell.execute_reply":"2022-07-27T08:43:54.228085Z"},"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\nsales = home_data.SalePrice\nhome_data.columns","metadata":{"execution":{"iopub.status.busy":"2022-07-27T08:49:52.977910Z","iopub.execute_input":"2022-07-27T08:49:52.978420Z","iopub.status.idle":"2022-07-27T08:49:52.989647Z","shell.execute_reply.started":"2022-07-27T08:49:52.978374Z","shell.execute_reply":"2022-07-27T08:49:52.988073Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y = sales\n\n# Check your answer\nstep_1.check()","metadata":{"execution":{"iopub.status.busy":"2022-07-27T08:44:00.882840Z","iopub.execute_input":"2022-07-27T08:44:00.883220Z","iopub.status.idle":"2022-07-27T08:44:00.895731Z","shell.execute_reply.started":"2022-07-27T08:44:00.883190Z","shell.execute_reply":"2022-07-27T08:44:00.894221Z"},"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":{"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":"2022-07-27T08:44:46.105662Z","iopub.execute_input":"2022-07-27T08:44:46.106096Z","iopub.status.idle":"2022-07-27T08:44:46.118083Z","shell.execute_reply.started":"2022-07-27T08:44:46.106060Z","shell.execute_reply":"2022-07-27T08:44:46.116910Z"},"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\nhome_data.describe()\n\n# print the top few lines\nhome_data.head(6)","metadata":{"execution":{"iopub.status.busy":"2022-07-27T08:44:48.343165Z","iopub.execute_input":"2022-07-27T08:44:48.343977Z","iopub.status.idle":"2022-07-27T08:44:48.457406Z","shell.execute_reply.started":"2022-07-27T08:44:48.343927Z","shell.execute_reply":"2022-07-27T08:44:48.456526Z"},"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":"2022-07-27T08:44:52.210533Z","iopub.execute_input":"2022-07-27T08:44:52.210971Z","iopub.status.idle":"2022-07-27T08:44:52.234333Z","shell.execute_reply.started":"2022-07-27T08:44:52.210934Z","shell.execute_reply":"2022-07-27T08:44:52.232892Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# step_3.hint()\n# step_3.solution()","metadata":{"execution":{"iopub.status.busy":"2022-07-27T08:28:47.865607Z","iopub.execute_input":"2022-07-27T08:28:47.865975Z","iopub.status.idle":"2022-07-27T08:28:47.870945Z","shell.execute_reply.started":"2022-07-27T08:28:47.865945Z","shell.execute_reply":"2022-07-27T08:28:47.869795Z"},"trusted":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":"print(X.head())\n\npredictions = iowa_model.predict(X)\nprint(predictions)\n\n# Check your answer\nstep_4.check()","metadata":{"execution":{"iopub.status.busy":"2022-07-27T08:47:48.181532Z","iopub.execute_input":"2022-07-27T08:47:48.181958Z","iopub.status.idle":"2022-07-27T08:47:48.202233Z","shell.execute_reply.started":"2022-07-27T08:47:48.181921Z","shell.execute_reply":"2022-07-27T08:47:48.200968Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# step_4.hint()\n# step_4.solution()","metadata":{"execution":{"iopub.status.busy":"2022-07-27T08:47:28.918571Z","iopub.execute_input":"2022-07-27T08:47:28.918970Z","iopub.status.idle":"2022-07-27T08:47:28.924623Z","shell.execute_reply.started":"2022-07-27T08:47:28.918933Z","shell.execute_reply":"2022-07-27T08:47:28.923184Z"},"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":"# You can write code in this cell\nprint(y.head())","metadata":{"execution":{"iopub.status.busy":"2022-07-27T08:53:55.409019Z","iopub.execute_input":"2022-07-27T08:53:55.409401Z","iopub.status.idle":"2022-07-27T08:53:55.415873Z","shell.execute_reply.started":"2022-07-27T08:53:55.409370Z","shell.execute_reply":"2022-07-27T08:53:55.414884Z"},"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 [course discussion forum](https://www.kaggle.com/learn/intro-to-machine-learning/discussion) to chat with other learners.*","metadata":{}}]}