├── README.md ├── demo.ipynb └── data └── data_description.txt /README.md: -------------------------------------------------------------------------------- 1 | # Demo from Data Community Bydgoszcz i Toruń, 27.02.2019 2 | 3 | ## Preparation 4 | ``` 5 | virtualenv -p python3 dcbit_env 6 | source dcbit_env/bin/activate 7 | pip install jupyterlab 8 | ``` 9 | 10 | ## Working with this demo 11 | 12 | 1. Clone this repository 13 | 2. Make sure `dcbit_env` is activated 14 | 3. Type `jupyter lab` 15 | 4. Open `demo.ipynb` 16 | 5. Run the first cell 17 | 6. If a module is missing install it by running `!pip install NAME` in a new cell 18 | 7. Enjoy the demo 19 | 20 | ## Alien vs. Predator Keras and PyTorch demo 21 | 22 | [Blog post](https://medium.freecodecamp.org/keras-vs-pytorch-avp-transfer-learning-c8b852c31f02) 23 | 24 | https://app.neptune.ml/o/deepsense-ai/org/Keras-vs-PyTorch/experiments 25 | 26 | ## Recording of the meeting in February 2019 (in Polish) 27 | 28 | https://www.youtube.com/watch?v=7bU_4QBubGA 29 | 30 | ## Useful links 31 | 32 | https://github.com/buus2/dcbit/wiki/Useful-links 33 | -------------------------------------------------------------------------------- /demo.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import pandas_profiling as pp\n", 11 | "\n", 12 | "import seaborn as sns\n", 13 | "sns.set()\n", 14 | "import matplotlib.pyplot as plt\n", 15 | "%matplotlib inline\n", 16 | "\n", 17 | "import numpy as np\n", 18 | "\n", 19 | "import itertools\n", 20 | "\n", 21 | "from sklearn.model_selection import train_test_split\n", 22 | "from sklearn.metrics import mean_squared_error, r2_score\n", 23 | "from sklearn.linear_model import Ridge\n", 24 | "\n", 25 | "import lightgbm as lgb" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "# Data exploration" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": null, 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [ 41 | "df = pd.read_csv(\"data/ames.csv\")\n", 42 | "df = df[[\"LotArea\", \"OverallQual\", \"OverallCond\", \"YearBuilt\", \"TotalBsmtSF\",\n", 43 | " \"1stFlrSF\", \"2ndFlrSF\", \"Fireplaces\", \"GarageCars\", \"OpenPorchSF\", \"SalePrice\"]]" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": null, 49 | "metadata": {}, 50 | "outputs": [], 51 | "source": [ 52 | "df.head()" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": null, 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [ 61 | "df.info()" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": null, 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "df.describe()" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": null, 76 | "metadata": {}, 77 | "outputs": [], 78 | "source": [ 79 | "df[\"SalePrice\"].hist()" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": null, 85 | "metadata": {}, 86 | "outputs": [], 87 | "source": [ 88 | "sns.clustermap(df.corr());" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": null, 94 | "metadata": {}, 95 | "outputs": [], 96 | "source": [ 97 | "sns.catplot(data=df,\n", 98 | " x=\"GarageCars\", y=\"SalePrice\",\n", 99 | " col=\"Fireplaces\",\n", 100 | " kind=\"bar\",\n", 101 | " col_wrap=2);" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": null, 107 | "metadata": {}, 108 | "outputs": [], 109 | "source": [ 110 | "pp.ProfileReport(df).to_file(output_file=\"report.html\")" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "# Regression task" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": null, 123 | "metadata": {}, 124 | "outputs": [], 125 | "source": [ 126 | "X = df.drop(\"SalePrice\", axis=1)\n", 127 | "Y = df[\"SalePrice\"]" 128 | ] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "metadata": {}, 133 | "source": [ 134 | "## Train/test split" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": null, 140 | "metadata": {}, 141 | "outputs": [], 142 | "source": [ 143 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=12)" 144 | ] 145 | }, 146 | { 147 | "cell_type": "code", 148 | "execution_count": null, 149 | "metadata": {}, 150 | "outputs": [], 151 | "source": [ 152 | "len(X), len(X_train), len(Y_test)" 153 | ] 154 | }, 155 | { 156 | "cell_type": "markdown", 157 | "metadata": {}, 158 | "source": [ 159 | "## Approach 0: constant model" 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": null, 165 | "metadata": {}, 166 | "outputs": [], 167 | "source": [ 168 | "Y_pred_const = np.full(Y_test.shape, Y_train.mean())\n", 169 | "Y_pred_const[:3]" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": null, 175 | "metadata": {}, 176 | "outputs": [], 177 | "source": [ 178 | "np.sqrt(mean_squared_error(Y_test, Y_pred_const))" 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": null, 184 | "metadata": {}, 185 | "outputs": [], 186 | "source": [ 187 | "r2_score(Y_test, Y_pred_const)" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": null, 193 | "metadata": {}, 194 | "outputs": [], 195 | "source": [ 196 | "def plot_predictions(Y_true, Y_predicted):\n", 197 | " df_pred = pd.DataFrame({\"true\": Y_true, \"predicted\": Y_predicted})\n", 198 | " val_min = 0\n", 199 | " val_max = 1000000\n", 200 | " figure = sns.jointplot('true', 'predicted', df_pred,\n", 201 | " xlim=(val_min, val_max),\n", 202 | " ylim=(val_min, val_max))\n", 203 | " figure.ax_joint.plot([val_min, val_max], [val_min, val_max], ':k') \n", 204 | " figure.fig.set_figheight(8)\n", 205 | " figure.fig.set_figwidth(8)" 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "execution_count": null, 211 | "metadata": {}, 212 | "outputs": [], 213 | "source": [ 214 | "plot_predictions(Y_test, Y_pred_const)" 215 | ] 216 | }, 217 | { 218 | "cell_type": "markdown", 219 | "metadata": {}, 220 | "source": [ 221 | "## Approach 1: linear regression model" 222 | ] 223 | }, 224 | { 225 | "cell_type": "markdown", 226 | "metadata": {}, 227 | "source": [ 228 | "### Preprocessing" 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": null, 234 | "metadata": {}, 235 | "outputs": [], 236 | "source": [ 237 | "X_train_lr = X_train.copy()\n", 238 | "X_test_lr = X_test.copy()" 239 | ] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": null, 244 | "metadata": {}, 245 | "outputs": [], 246 | "source": [ 247 | "X_train_lr[\"LotArea\"] = np.log10(X_train_lr[\"LotArea\"])\n", 248 | "X_test_lr[\"LotArea\"] = np.log10(X_test_lr[\"LotArea\"])\n", 249 | "\n", 250 | "X_train_lr[\"OpenPorchSF\"] = [np.log10(value) if value > 0 else 0 for value in X_train_lr[\"OpenPorchSF\"]]\n", 251 | "X_test_lr[\"OpenPorchSF\"] = [np.log10(value) if value > 0 else 0 for value in X_test_lr[\"OpenPorchSF\"]]" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": null, 257 | "metadata": {}, 258 | "outputs": [], 259 | "source": [ 260 | "columns = X_train_lr.columns\n", 261 | "\n", 262 | "for col1, col2 in itertools.combinations(columns, 2):\n", 263 | " X_train_lr[col1 + '_x_' + col2] = X_train_lr[col1] * X_train_lr[col2]\n", 264 | " X_test_lr[col1 + '_x_' + col2] = X_test_lr[col1] * X_test_lr[col2]\n", 265 | " \n", 266 | "for col in columns:\n", 267 | " X_train_lr[col + '^2'] = X_train_lr[col] ** 2\n", 268 | " X_test_lr[col + '^2'] = X_test_lr[col] ** 2" 269 | ] 270 | }, 271 | { 272 | "cell_type": "code", 273 | "execution_count": null, 274 | "metadata": {}, 275 | "outputs": [], 276 | "source": [ 277 | "X_test_lr = (X_test_lr - X_train_lr.mean()) / X_train_lr.std()\n", 278 | "X_train_lr = (X_train_lr - X_train_lr.mean()) / X_train_lr.std()" 279 | ] 280 | }, 281 | { 282 | "cell_type": "code", 283 | "execution_count": null, 284 | "metadata": {}, 285 | "outputs": [], 286 | "source": [ 287 | "X_train_lr.head()" 288 | ] 289 | }, 290 | { 291 | "cell_type": "markdown", 292 | "metadata": {}, 293 | "source": [ 294 | "### Training" 295 | ] 296 | }, 297 | { 298 | "cell_type": "code", 299 | "execution_count": null, 300 | "metadata": {}, 301 | "outputs": [], 302 | "source": [ 303 | "reg = Ridge(random_state=12)" 304 | ] 305 | }, 306 | { 307 | "cell_type": "code", 308 | "execution_count": null, 309 | "metadata": {}, 310 | "outputs": [], 311 | "source": [ 312 | "reg.fit(X_train_lr, Y_train)" 313 | ] 314 | }, 315 | { 316 | "cell_type": "code", 317 | "execution_count": null, 318 | "metadata": {}, 319 | "outputs": [], 320 | "source": [ 321 | "Y_pred_lr = reg.predict(X_test_lr)" 322 | ] 323 | }, 324 | { 325 | "cell_type": "code", 326 | "execution_count": null, 327 | "metadata": {}, 328 | "outputs": [], 329 | "source": [ 330 | "np.sqrt(mean_squared_error(Y_test, Y_pred_lr))" 331 | ] 332 | }, 333 | { 334 | "cell_type": "code", 335 | "execution_count": null, 336 | "metadata": {}, 337 | "outputs": [], 338 | "source": [ 339 | "r2_score(Y_test, Y_pred_lr)" 340 | ] 341 | }, 342 | { 343 | "cell_type": "code", 344 | "execution_count": null, 345 | "metadata": {}, 346 | "outputs": [], 347 | "source": [ 348 | "plot_predictions(Y_test, Y_pred_lr)" 349 | ] 350 | }, 351 | { 352 | "cell_type": "markdown", 353 | "metadata": {}, 354 | "source": [ 355 | "## Approach 2: LightGBM" 356 | ] 357 | }, 358 | { 359 | "cell_type": "markdown", 360 | "metadata": {}, 361 | "source": [ 362 | "### No preprocessing, just training" 363 | ] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "execution_count": null, 368 | "metadata": {}, 369 | "outputs": [], 370 | "source": [ 371 | "lgb_model = lgb.LGBMRegressor(random_state=12)" 372 | ] 373 | }, 374 | { 375 | "cell_type": "code", 376 | "execution_count": null, 377 | "metadata": {}, 378 | "outputs": [], 379 | "source": [ 380 | "lgb_model.fit(X_train, Y_train)" 381 | ] 382 | }, 383 | { 384 | "cell_type": "code", 385 | "execution_count": null, 386 | "metadata": {}, 387 | "outputs": [], 388 | "source": [ 389 | "Y_pred_lgb = lgb_model.predict(X_test)" 390 | ] 391 | }, 392 | { 393 | "cell_type": "code", 394 | "execution_count": null, 395 | "metadata": {}, 396 | "outputs": [], 397 | "source": [ 398 | "np.sqrt(mean_squared_error(Y_test, Y_pred_lgb))" 399 | ] 400 | }, 401 | { 402 | "cell_type": "code", 403 | "execution_count": null, 404 | "metadata": {}, 405 | "outputs": [], 406 | "source": [ 407 | "r2_score(Y_test, Y_pred_lgb)" 408 | ] 409 | }, 410 | { 411 | "cell_type": "code", 412 | "execution_count": null, 413 | "metadata": {}, 414 | "outputs": [], 415 | "source": [ 416 | "plot_predictions(Y_test, Y_pred_lgb)" 417 | ] 418 | }, 419 | { 420 | "cell_type": "code", 421 | "execution_count": null, 422 | "metadata": {}, 423 | "outputs": [], 424 | "source": [] 425 | } 426 | ], 427 | "metadata": { 428 | "kernelspec": { 429 | "display_name": "Python 3", 430 | "language": "python", 431 | "name": "python3" 432 | }, 433 | "language_info": { 434 | "codemirror_mode": { 435 | "name": "ipython", 436 | "version": 3 437 | }, 438 | "file_extension": ".py", 439 | "mimetype": "text/x-python", 440 | "name": "python", 441 | "nbconvert_exporter": "python", 442 | "pygments_lexer": "ipython3", 443 | "version": "3.6.8" 444 | } 445 | }, 446 | "nbformat": 4, 447 | "nbformat_minor": 4 448 | } 449 | -------------------------------------------------------------------------------- /data/data_description.txt: -------------------------------------------------------------------------------- 1 | MSSubClass: Identifies the type of dwelling involved in the sale. 2 | 3 | 20 1-STORY 1946 & NEWER ALL STYLES 4 | 30 1-STORY 1945 & OLDER 5 | 40 1-STORY W/FINISHED ATTIC ALL AGES 6 | 45 1-1/2 STORY - UNFINISHED ALL AGES 7 | 50 1-1/2 STORY FINISHED ALL AGES 8 | 60 2-STORY 1946 & NEWER 9 | 70 2-STORY 1945 & OLDER 10 | 75 2-1/2 STORY ALL AGES 11 | 80 SPLIT OR MULTI-LEVEL 12 | 85 SPLIT FOYER 13 | 90 DUPLEX - ALL STYLES AND AGES 14 | 120 1-STORY PUD (Planned Unit Development) - 1946 & NEWER 15 | 150 1-1/2 STORY PUD - ALL AGES 16 | 160 2-STORY PUD - 1946 & NEWER 17 | 180 PUD - MULTILEVEL - INCL SPLIT LEV/FOYER 18 | 190 2 FAMILY CONVERSION - ALL STYLES AND AGES 19 | 20 | MSZoning: Identifies the general zoning classification of the sale. 21 | 22 | A Agriculture 23 | C Commercial 24 | FV Floating Village Residential 25 | I Industrial 26 | RH Residential High Density 27 | RL Residential Low Density 28 | RP Residential Low Density Park 29 | RM Residential Medium Density 30 | 31 | LotFrontage: Linear feet of street connected to property 32 | 33 | LotArea: Lot size in square feet 34 | 35 | Street: Type of road access to property 36 | 37 | Grvl Gravel 38 | Pave Paved 39 | 40 | Alley: Type of alley access to property 41 | 42 | Grvl Gravel 43 | Pave Paved 44 | NA No alley access 45 | 46 | LotShape: General shape of property 47 | 48 | Reg Regular 49 | IR1 Slightly irregular 50 | IR2 Moderately Irregular 51 | IR3 Irregular 52 | 53 | LandContour: Flatness of the property 54 | 55 | Lvl Near Flat/Level 56 | Bnk Banked - Quick and significant rise from street grade to building 57 | HLS Hillside - Significant slope from side to side 58 | Low Depression 59 | 60 | Utilities: Type of utilities available 61 | 62 | AllPub All public Utilities (E,G,W,& S) 63 | NoSewr Electricity, Gas, and Water (Septic Tank) 64 | NoSeWa Electricity and Gas Only 65 | ELO Electricity only 66 | 67 | LotConfig: Lot configuration 68 | 69 | Inside Inside lot 70 | Corner Corner lot 71 | CulDSac Cul-de-sac 72 | FR2 Frontage on 2 sides of property 73 | FR3 Frontage on 3 sides of property 74 | 75 | LandSlope: Slope of property 76 | 77 | Gtl Gentle slope 78 | Mod Moderate Slope 79 | Sev Severe Slope 80 | 81 | Neighborhood: Physical locations within Ames city limits 82 | 83 | Blmngtn Bloomington Heights 84 | Blueste Bluestem 85 | BrDale Briardale 86 | BrkSide Brookside 87 | ClearCr Clear Creek 88 | CollgCr College Creek 89 | Crawfor Crawford 90 | Edwards Edwards 91 | Gilbert Gilbert 92 | IDOTRR Iowa DOT and Rail Road 93 | MeadowV Meadow Village 94 | Mitchel Mitchell 95 | Names North Ames 96 | NoRidge Northridge 97 | NPkVill Northpark Villa 98 | NridgHt Northridge Heights 99 | NWAmes Northwest Ames 100 | OldTown Old Town 101 | SWISU South & West of Iowa State University 102 | Sawyer Sawyer 103 | SawyerW Sawyer West 104 | Somerst Somerset 105 | StoneBr Stone Brook 106 | Timber Timberland 107 | Veenker Veenker 108 | 109 | Condition1: Proximity to various conditions 110 | 111 | Artery Adjacent to arterial street 112 | Feedr Adjacent to feeder street 113 | Norm Normal 114 | RRNn Within 200' of North-South Railroad 115 | RRAn Adjacent to North-South Railroad 116 | PosN Near positive off-site feature--park, greenbelt, etc. 117 | PosA Adjacent to postive off-site feature 118 | RRNe Within 200' of East-West Railroad 119 | RRAe Adjacent to East-West Railroad 120 | 121 | Condition2: Proximity to various conditions (if more than one is present) 122 | 123 | Artery Adjacent to arterial street 124 | Feedr Adjacent to feeder street 125 | Norm Normal 126 | RRNn Within 200' of North-South Railroad 127 | RRAn Adjacent to North-South Railroad 128 | PosN Near positive off-site feature--park, greenbelt, etc. 129 | PosA Adjacent to postive off-site feature 130 | RRNe Within 200' of East-West Railroad 131 | RRAe Adjacent to East-West Railroad 132 | 133 | BldgType: Type of dwelling 134 | 135 | 1Fam Single-family Detached 136 | 2FmCon Two-family Conversion; originally built as one-family dwelling 137 | Duplx Duplex 138 | TwnhsE Townhouse End Unit 139 | TwnhsI Townhouse Inside Unit 140 | 141 | HouseStyle: Style of dwelling 142 | 143 | 1Story One story 144 | 1.5Fin One and one-half story: 2nd level finished 145 | 1.5Unf One and one-half story: 2nd level unfinished 146 | 2Story Two story 147 | 2.5Fin Two and one-half story: 2nd level finished 148 | 2.5Unf Two and one-half story: 2nd level unfinished 149 | SFoyer Split Foyer 150 | SLvl Split Level 151 | 152 | OverallQual: Rates the overall material and finish of the house 153 | 154 | 10 Very Excellent 155 | 9 Excellent 156 | 8 Very Good 157 | 7 Good 158 | 6 Above Average 159 | 5 Average 160 | 4 Below Average 161 | 3 Fair 162 | 2 Poor 163 | 1 Very Poor 164 | 165 | OverallCond: Rates the overall condition of the house 166 | 167 | 10 Very Excellent 168 | 9 Excellent 169 | 8 Very Good 170 | 7 Good 171 | 6 Above Average 172 | 5 Average 173 | 4 Below Average 174 | 3 Fair 175 | 2 Poor 176 | 1 Very Poor 177 | 178 | YearBuilt: Original construction date 179 | 180 | YearRemodAdd: Remodel date (same as construction date if no remodeling or additions) 181 | 182 | RoofStyle: Type of roof 183 | 184 | Flat Flat 185 | Gable Gable 186 | Gambrel Gabrel (Barn) 187 | Hip Hip 188 | Mansard Mansard 189 | Shed Shed 190 | 191 | RoofMatl: Roof material 192 | 193 | ClyTile Clay or Tile 194 | CompShg Standard (Composite) Shingle 195 | Membran Membrane 196 | Metal Metal 197 | Roll Roll 198 | Tar&Grv Gravel & Tar 199 | WdShake Wood Shakes 200 | WdShngl Wood Shingles 201 | 202 | Exterior1st: Exterior covering on house 203 | 204 | AsbShng Asbestos Shingles 205 | AsphShn Asphalt Shingles 206 | BrkComm Brick Common 207 | BrkFace Brick Face 208 | CBlock Cinder Block 209 | CemntBd Cement Board 210 | HdBoard Hard Board 211 | ImStucc Imitation Stucco 212 | MetalSd Metal Siding 213 | Other Other 214 | Plywood Plywood 215 | PreCast PreCast 216 | Stone Stone 217 | Stucco Stucco 218 | VinylSd Vinyl Siding 219 | Wd Sdng Wood Siding 220 | WdShing Wood Shingles 221 | 222 | Exterior2nd: Exterior covering on house (if more than one material) 223 | 224 | AsbShng Asbestos Shingles 225 | AsphShn Asphalt Shingles 226 | BrkComm Brick Common 227 | BrkFace Brick Face 228 | CBlock Cinder Block 229 | CemntBd Cement Board 230 | HdBoard Hard Board 231 | ImStucc Imitation Stucco 232 | MetalSd Metal Siding 233 | Other Other 234 | Plywood Plywood 235 | PreCast PreCast 236 | Stone Stone 237 | Stucco Stucco 238 | VinylSd Vinyl Siding 239 | Wd Sdng Wood Siding 240 | WdShing Wood Shingles 241 | 242 | MasVnrType: Masonry veneer type 243 | 244 | BrkCmn Brick Common 245 | BrkFace Brick Face 246 | CBlock Cinder Block 247 | None None 248 | Stone Stone 249 | 250 | MasVnrArea: Masonry veneer area in square feet 251 | 252 | ExterQual: Evaluates the quality of the material on the exterior 253 | 254 | Ex Excellent 255 | Gd Good 256 | TA Average/Typical 257 | Fa Fair 258 | Po Poor 259 | 260 | ExterCond: Evaluates the present condition of the material on the exterior 261 | 262 | Ex Excellent 263 | Gd Good 264 | TA Average/Typical 265 | Fa Fair 266 | Po Poor 267 | 268 | Foundation: Type of foundation 269 | 270 | BrkTil Brick & Tile 271 | CBlock Cinder Block 272 | PConc Poured Contrete 273 | Slab Slab 274 | Stone Stone 275 | Wood Wood 276 | 277 | BsmtQual: Evaluates the height of the basement 278 | 279 | Ex Excellent (100+ inches) 280 | Gd Good (90-99 inches) 281 | TA Typical (80-89 inches) 282 | Fa Fair (70-79 inches) 283 | Po Poor (<70 inches 284 | NA No Basement 285 | 286 | BsmtCond: Evaluates the general condition of the basement 287 | 288 | Ex Excellent 289 | Gd Good 290 | TA Typical - slight dampness allowed 291 | Fa Fair - dampness or some cracking or settling 292 | Po Poor - Severe cracking, settling, or wetness 293 | NA No Basement 294 | 295 | BsmtExposure: Refers to walkout or garden level walls 296 | 297 | Gd Good Exposure 298 | Av Average Exposure (split levels or foyers typically score average or above) 299 | Mn Mimimum Exposure 300 | No No Exposure 301 | NA No Basement 302 | 303 | BsmtFinType1: Rating of basement finished area 304 | 305 | GLQ Good Living Quarters 306 | ALQ Average Living Quarters 307 | BLQ Below Average Living Quarters 308 | Rec Average Rec Room 309 | LwQ Low Quality 310 | Unf Unfinshed 311 | NA No Basement 312 | 313 | BsmtFinSF1: Type 1 finished square feet 314 | 315 | BsmtFinType2: Rating of basement finished area (if multiple types) 316 | 317 | GLQ Good Living Quarters 318 | ALQ Average Living Quarters 319 | BLQ Below Average Living Quarters 320 | Rec Average Rec Room 321 | LwQ Low Quality 322 | Unf Unfinshed 323 | NA No Basement 324 | 325 | BsmtFinSF2: Type 2 finished square feet 326 | 327 | BsmtUnfSF: Unfinished square feet of basement area 328 | 329 | TotalBsmtSF: Total square feet of basement area 330 | 331 | Heating: Type of heating 332 | 333 | Floor Floor Furnace 334 | GasA Gas forced warm air furnace 335 | GasW Gas hot water or steam heat 336 | Grav Gravity furnace 337 | OthW Hot water or steam heat other than gas 338 | Wall Wall furnace 339 | 340 | HeatingQC: Heating quality and condition 341 | 342 | Ex Excellent 343 | Gd Good 344 | TA Average/Typical 345 | Fa Fair 346 | Po Poor 347 | 348 | CentralAir: Central air conditioning 349 | 350 | N No 351 | Y Yes 352 | 353 | Electrical: Electrical system 354 | 355 | SBrkr Standard Circuit Breakers & Romex 356 | FuseA Fuse Box over 60 AMP and all Romex wiring (Average) 357 | FuseF 60 AMP Fuse Box and mostly Romex wiring (Fair) 358 | FuseP 60 AMP Fuse Box and mostly knob & tube wiring (poor) 359 | Mix Mixed 360 | 361 | 1stFlrSF: First Floor square feet 362 | 363 | 2ndFlrSF: Second floor square feet 364 | 365 | LowQualFinSF: Low quality finished square feet (all floors) 366 | 367 | GrLivArea: Above grade (ground) living area square feet 368 | 369 | BsmtFullBath: Basement full bathrooms 370 | 371 | BsmtHalfBath: Basement half bathrooms 372 | 373 | FullBath: Full bathrooms above grade 374 | 375 | HalfBath: Half baths above grade 376 | 377 | Bedroom: Bedrooms above grade (does NOT include basement bedrooms) 378 | 379 | Kitchen: Kitchens above grade 380 | 381 | KitchenQual: Kitchen quality 382 | 383 | Ex Excellent 384 | Gd Good 385 | TA Typical/Average 386 | Fa Fair 387 | Po Poor 388 | 389 | TotRmsAbvGrd: Total rooms above grade (does not include bathrooms) 390 | 391 | Functional: Home functionality (Assume typical unless deductions are warranted) 392 | 393 | Typ Typical Functionality 394 | Min1 Minor Deductions 1 395 | Min2 Minor Deductions 2 396 | Mod Moderate Deductions 397 | Maj1 Major Deductions 1 398 | Maj2 Major Deductions 2 399 | Sev Severely Damaged 400 | Sal Salvage only 401 | 402 | Fireplaces: Number of fireplaces 403 | 404 | FireplaceQu: Fireplace quality 405 | 406 | Ex Excellent - Exceptional Masonry Fireplace 407 | Gd Good - Masonry Fireplace in main level 408 | TA Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basement 409 | Fa Fair - Prefabricated Fireplace in basement 410 | Po Poor - Ben Franklin Stove 411 | NA No Fireplace 412 | 413 | GarageType: Garage location 414 | 415 | 2Types More than one type of garage 416 | Attchd Attached to home 417 | Basment Basement Garage 418 | BuiltIn Built-In (Garage part of house - typically has room above garage) 419 | CarPort Car Port 420 | Detchd Detached from home 421 | NA No Garage 422 | 423 | GarageYrBlt: Year garage was built 424 | 425 | GarageFinish: Interior finish of the garage 426 | 427 | Fin Finished 428 | RFn Rough Finished 429 | Unf Unfinished 430 | NA No Garage 431 | 432 | GarageCars: Size of garage in car capacity 433 | 434 | GarageArea: Size of garage in square feet 435 | 436 | GarageQual: Garage quality 437 | 438 | Ex Excellent 439 | Gd Good 440 | TA Typical/Average 441 | Fa Fair 442 | Po Poor 443 | NA No Garage 444 | 445 | GarageCond: Garage condition 446 | 447 | Ex Excellent 448 | Gd Good 449 | TA Typical/Average 450 | Fa Fair 451 | Po Poor 452 | NA No Garage 453 | 454 | PavedDrive: Paved driveway 455 | 456 | Y Paved 457 | P Partial Pavement 458 | N Dirt/Gravel 459 | 460 | WoodDeckSF: Wood deck area in square feet 461 | 462 | OpenPorchSF: Open porch area in square feet 463 | 464 | EnclosedPorch: Enclosed porch area in square feet 465 | 466 | 3SsnPorch: Three season porch area in square feet 467 | 468 | ScreenPorch: Screen porch area in square feet 469 | 470 | PoolArea: Pool area in square feet 471 | 472 | PoolQC: Pool quality 473 | 474 | Ex Excellent 475 | Gd Good 476 | TA Average/Typical 477 | Fa Fair 478 | NA No Pool 479 | 480 | Fence: Fence quality 481 | 482 | GdPrv Good Privacy 483 | MnPrv Minimum Privacy 484 | GdWo Good Wood 485 | MnWw Minimum Wood/Wire 486 | NA No Fence 487 | 488 | MiscFeature: Miscellaneous feature not covered in other categories 489 | 490 | Elev Elevator 491 | Gar2 2nd Garage (if not described in garage section) 492 | Othr Other 493 | Shed Shed (over 100 SF) 494 | TenC Tennis Court 495 | NA None 496 | 497 | MiscVal: $Value of miscellaneous feature 498 | 499 | MoSold: Month Sold (MM) 500 | 501 | YrSold: Year Sold (YYYY) 502 | 503 | SaleType: Type of sale 504 | 505 | WD Warranty Deed - Conventional 506 | CWD Warranty Deed - Cash 507 | VWD Warranty Deed - VA Loan 508 | New Home just constructed and sold 509 | COD Court Officer Deed/Estate 510 | Con Contract 15% Down payment regular terms 511 | ConLw Contract Low Down payment and low interest 512 | ConLI Contract Low Interest 513 | ConLD Contract Low Down 514 | Oth Other 515 | 516 | SaleCondition: Condition of sale 517 | 518 | Normal Normal Sale 519 | Abnorml Abnormal Sale - trade, foreclosure, short sale 520 | AdjLand Adjoining Land Purchase 521 | Alloca Allocation - two linked properties with separate deeds, typically condo with a garage unit 522 | Family Sale between family members 523 | Partial Home was not completed when last assessed (associated with New Homes) 524 | --------------------------------------------------------------------------------