{"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":"2022-07-20T16:18:29.035440Z","iopub.execute_input":"2022-07-20T16:18:29.036157Z","iopub.status.idle":"2022-07-20T16:18:30.602441Z","shell.execute_reply.started":"2022-07-20T16:18:29.036038Z","shell.execute_reply":"2022-07-20T16:18:30.601219Z"},"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":"2022-07-20T16:19:07.301382Z","iopub.execute_input":"2022-07-20T16:19:07.301801Z","iopub.status.idle":"2022-07-20T16:19:07.313881Z","shell.execute_reply.started":"2022-07-20T16:19:07.301767Z","shell.execute_reply":"2022-07-20T16:19:07.312497Z"},"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":"2022-07-20T16:19:52.999135Z","iopub.execute_input":"2022-07-20T16:19:52.999720Z","iopub.status.idle":"2022-07-20T16:19:53.010901Z","shell.execute_reply.started":"2022-07-20T16:19:52.999670Z","shell.execute_reply":"2022-07-20T16:19:53.009788Z"},"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() \nstep_1.solution()","metadata":{"execution":{"iopub.status.busy":"2022-07-20T16:19:31.438758Z","iopub.execute_input":"2022-07-20T16:19:31.439189Z","iopub.status.idle":"2022-07-20T16:19:31.449157Z","shell.execute_reply.started":"2022-07-20T16:19:31.439155Z","shell.execute_reply":"2022-07-20T16:19:31.448038Z"},"trusted":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\",\n                      \"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":"2022-07-20T16:52:54.587147Z","iopub.execute_input":"2022-07-20T16:52:54.587551Z","iopub.status.idle":"2022-07-20T16:52:54.605221Z","shell.execute_reply.started":"2022-07-20T16:52:54.587521Z","shell.execute_reply":"2022-07-20T16:52:54.604354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# step_2.hint()\nstep_2.solution()","metadata":{"execution":{"iopub.status.busy":"2022-07-20T16:50:20.428111Z","iopub.execute_input":"2022-07-20T16:50:20.428533Z","iopub.status.idle":"2022-07-20T16:50:20.439548Z","shell.execute_reply.started":"2022-07-20T16:50:20.428500Z","shell.execute_reply":"2022-07-20T16:50:20.438020Z"},"trusted":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(home_data.describe())\n\n# print the top few lines\nprint(home_data.head())","metadata":{"execution":{"iopub.status.busy":"2022-07-20T16:56:50.198457Z","iopub.execute_input":"2022-07-20T16:56:50.198889Z","iopub.status.idle":"2022-07-20T16:56:50.305225Z","shell.execute_reply.started":"2022-07-20T16:56:50.198854Z","shell.execute_reply":"2022-07-20T16:56:50.303954Z"},"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 _ import _\n#specify the model. \n#For model reproducibility, set a numeric value for random_state when specifying the model\n\nfrom sklearn.tree import DecisionTreeRegressor\niowa_model = DecisionTreeRegressor(random_state=1)\n# Fit the model\niowa_model.fit(X, y)\n\n\n# Check your answer\nstep_3.check()","metadata":{"execution":{"iopub.status.busy":"2022-07-20T17:06:33.218590Z","iopub.execute_input":"2022-07-20T17:06:33.219011Z","iopub.status.idle":"2022-07-20T17:06:33.243861Z","shell.execute_reply.started":"2022-07-20T17:06:33.218974Z","shell.execute_reply":"2022-07-20T17:06:33.243038Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# step_3.hint()\n#step_3.solution()","metadata":{"execution":{"iopub.status.busy":"2022-07-20T17:07:20.880118Z","iopub.execute_input":"2022-07-20T17:07:20.880590Z","iopub.status.idle":"2022-07-20T17:07:20.885230Z","shell.execute_reply.started":"2022-07-20T17:07:20.880556Z","shell.execute_reply":"2022-07-20T17:07:20.884051Z"},"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":"predictions = iowa_model.predict(X)\nprint(predictions)\n\n# Check your answer\nstep_4.check()","metadata":{"execution":{"iopub.status.busy":"2022-07-20T17:15:56.823850Z","iopub.execute_input":"2022-07-20T17:15:56.824235Z","iopub.status.idle":"2022-07-20T17:15:56.838080Z","shell.execute_reply.started":"2022-07-20T17:15:56.824206Z","shell.execute_reply":"2022-07-20T17:15:56.837024Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# step_4.hint()\nstep_4.solution()","metadata":{"execution":{"iopub.status.busy":"2022-07-20T17:15:48.432610Z","iopub.execute_input":"2022-07-20T17:15:48.433607Z","iopub.status.idle":"2022-07-20T17:15:48.441539Z","shell.execute_reply.started":"2022-07-20T17:15:48.433564Z","shell.execute_reply":"2022-07-20T17:15:48.440481Z"},"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\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":{}}]}