├── DataPreprocessing.ipynb
├── Data_preprocessing.ipynb
├── Procfile
├── README.md
├── app.py
├── car data.csv
├── index.html
├── model.pkl
├── requirements.txt
└── templates
    └── index.html


/DataPreprocessing.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "import pandas as pd"
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "code",
  14 |    "execution_count": 2,
  15 |    "metadata": {},
  16 |    "outputs": [],
  17 |    "source": [
  18 |     "df=pd.read_csv('car data.csv')"
  19 |    ]
  20 |   },
  21 |   {
  22 |    "cell_type": "code",
  23 |    "execution_count": 3,
  24 |    "metadata": {},
  25 |    "outputs": [
  26 |     {
  27 |      "data": {
  28 |       "text/html": [
  29 |        "<div>\n",
  30 |        "<style scoped>\n",
  31 |        "    .dataframe tbody tr th:only-of-type {\n",
  32 |        "        vertical-align: middle;\n",
  33 |        "    }\n",
  34 |        "\n",
  35 |        "    .dataframe tbody tr th {\n",
  36 |        "        vertical-align: top;\n",
  37 |        "    }\n",
  38 |        "\n",
  39 |        "    .dataframe thead th {\n",
  40 |        "        text-align: right;\n",
  41 |        "    }\n",
  42 |        "</style>\n",
  43 |        "<table border=\"1\" class=\"dataframe\">\n",
  44 |        "  <thead>\n",
  45 |        "    <tr style=\"text-align: right;\">\n",
  46 |        "      <th></th>\n",
  47 |        "      <th>Car_Name</th>\n",
  48 |        "      <th>Year</th>\n",
  49 |        "      <th>Selling_Price</th>\n",
  50 |        "      <th>Present_Price</th>\n",
  51 |        "      <th>Kms_Driven</th>\n",
  52 |        "      <th>Fuel_Type</th>\n",
  53 |        "      <th>Seller_Type</th>\n",
  54 |        "      <th>Transmission</th>\n",
  55 |        "      <th>Owner</th>\n",
  56 |        "    </tr>\n",
  57 |        "  </thead>\n",
  58 |        "  <tbody>\n",
  59 |        "    <tr>\n",
  60 |        "      <th>0</th>\n",
  61 |        "      <td>ritz</td>\n",
  62 |        "      <td>2014</td>\n",
  63 |        "      <td>3.35</td>\n",
  64 |        "      <td>5.59</td>\n",
  65 |        "      <td>27000</td>\n",
  66 |        "      <td>Petrol</td>\n",
  67 |        "      <td>Dealer</td>\n",
  68 |        "      <td>Manual</td>\n",
  69 |        "      <td>0</td>\n",
  70 |        "    </tr>\n",
  71 |        "    <tr>\n",
  72 |        "      <th>1</th>\n",
  73 |        "      <td>sx4</td>\n",
  74 |        "      <td>2013</td>\n",
  75 |        "      <td>4.75</td>\n",
  76 |        "      <td>9.54</td>\n",
  77 |        "      <td>43000</td>\n",
  78 |        "      <td>Diesel</td>\n",
  79 |        "      <td>Dealer</td>\n",
  80 |        "      <td>Manual</td>\n",
  81 |        "      <td>0</td>\n",
  82 |        "    </tr>\n",
  83 |        "    <tr>\n",
  84 |        "      <th>2</th>\n",
  85 |        "      <td>ciaz</td>\n",
  86 |        "      <td>2017</td>\n",
  87 |        "      <td>7.25</td>\n",
  88 |        "      <td>9.85</td>\n",
  89 |        "      <td>6900</td>\n",
  90 |        "      <td>Petrol</td>\n",
  91 |        "      <td>Dealer</td>\n",
  92 |        "      <td>Manual</td>\n",
  93 |        "      <td>0</td>\n",
  94 |        "    </tr>\n",
  95 |        "    <tr>\n",
  96 |        "      <th>3</th>\n",
  97 |        "      <td>wagon r</td>\n",
  98 |        "      <td>2011</td>\n",
  99 |        "      <td>2.85</td>\n",
 100 |        "      <td>4.15</td>\n",
 101 |        "      <td>5200</td>\n",
 102 |        "      <td>Petrol</td>\n",
 103 |        "      <td>Dealer</td>\n",
 104 |        "      <td>Manual</td>\n",
 105 |        "      <td>0</td>\n",
 106 |        "    </tr>\n",
 107 |        "    <tr>\n",
 108 |        "      <th>4</th>\n",
 109 |        "      <td>swift</td>\n",
 110 |        "      <td>2014</td>\n",
 111 |        "      <td>4.60</td>\n",
 112 |        "      <td>6.87</td>\n",
 113 |        "      <td>42450</td>\n",
 114 |        "      <td>Diesel</td>\n",
 115 |        "      <td>Dealer</td>\n",
 116 |        "      <td>Manual</td>\n",
 117 |        "      <td>0</td>\n",
 118 |        "    </tr>\n",
 119 |        "  </tbody>\n",
 120 |        "</table>\n",
 121 |        "</div>"
 122 |       ],
 123 |       "text/plain": [
 124 |        "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
 125 |        "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
 126 |        "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
 127 |        "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
 128 |        "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
 129 |        "4    swift  2014           4.60           6.87       42450    Diesel   \n",
 130 |        "\n",
 131 |        "  Seller_Type Transmission  Owner  \n",
 132 |        "0      Dealer       Manual      0  \n",
 133 |        "1      Dealer       Manual      0  \n",
 134 |        "2      Dealer       Manual      0  \n",
 135 |        "3      Dealer       Manual      0  \n",
 136 |        "4      Dealer       Manual      0  "
 137 |       ]
 138 |      },
 139 |      "execution_count": 3,
 140 |      "metadata": {},
 141 |      "output_type": "execute_result"
 142 |     }
 143 |    ],
 144 |    "source": [
 145 |     "df.head()"
 146 |    ]
 147 |   },
 148 |   {
 149 |    "cell_type": "code",
 150 |    "execution_count": 4,
 151 |    "metadata": {},
 152 |    "outputs": [],
 153 |    "source": [
 154 |     "df['no_year']=2020-df['Year']"
 155 |    ]
 156 |   },
 157 |   {
 158 |    "cell_type": "code",
 159 |    "execution_count": 5,
 160 |    "metadata": {},
 161 |    "outputs": [
 162 |     {
 163 |      "data": {
 164 |       "text/html": [
 165 |        "<div>\n",
 166 |        "<style scoped>\n",
 167 |        "    .dataframe tbody tr th:only-of-type {\n",
 168 |        "        vertical-align: middle;\n",
 169 |        "    }\n",
 170 |        "\n",
 171 |        "    .dataframe tbody tr th {\n",
 172 |        "        vertical-align: top;\n",
 173 |        "    }\n",
 174 |        "\n",
 175 |        "    .dataframe thead th {\n",
 176 |        "        text-align: right;\n",
 177 |        "    }\n",
 178 |        "</style>\n",
 179 |        "<table border=\"1\" class=\"dataframe\">\n",
 180 |        "  <thead>\n",
 181 |        "    <tr style=\"text-align: right;\">\n",
 182 |        "      <th></th>\n",
 183 |        "      <th>Car_Name</th>\n",
 184 |        "      <th>Year</th>\n",
 185 |        "      <th>Selling_Price</th>\n",
 186 |        "      <th>Present_Price</th>\n",
 187 |        "      <th>Kms_Driven</th>\n",
 188 |        "      <th>Fuel_Type</th>\n",
 189 |        "      <th>Seller_Type</th>\n",
 190 |        "      <th>Transmission</th>\n",
 191 |        "      <th>Owner</th>\n",
 192 |        "      <th>no_year</th>\n",
 193 |        "    </tr>\n",
 194 |        "  </thead>\n",
 195 |        "  <tbody>\n",
 196 |        "    <tr>\n",
 197 |        "      <th>0</th>\n",
 198 |        "      <td>ritz</td>\n",
 199 |        "      <td>2014</td>\n",
 200 |        "      <td>3.35</td>\n",
 201 |        "      <td>5.59</td>\n",
 202 |        "      <td>27000</td>\n",
 203 |        "      <td>Petrol</td>\n",
 204 |        "      <td>Dealer</td>\n",
 205 |        "      <td>Manual</td>\n",
 206 |        "      <td>0</td>\n",
 207 |        "      <td>6</td>\n",
 208 |        "    </tr>\n",
 209 |        "    <tr>\n",
 210 |        "      <th>1</th>\n",
 211 |        "      <td>sx4</td>\n",
 212 |        "      <td>2013</td>\n",
 213 |        "      <td>4.75</td>\n",
 214 |        "      <td>9.54</td>\n",
 215 |        "      <td>43000</td>\n",
 216 |        "      <td>Diesel</td>\n",
 217 |        "      <td>Dealer</td>\n",
 218 |        "      <td>Manual</td>\n",
 219 |        "      <td>0</td>\n",
 220 |        "      <td>7</td>\n",
 221 |        "    </tr>\n",
 222 |        "    <tr>\n",
 223 |        "      <th>2</th>\n",
 224 |        "      <td>ciaz</td>\n",
 225 |        "      <td>2017</td>\n",
 226 |        "      <td>7.25</td>\n",
 227 |        "      <td>9.85</td>\n",
 228 |        "      <td>6900</td>\n",
 229 |        "      <td>Petrol</td>\n",
 230 |        "      <td>Dealer</td>\n",
 231 |        "      <td>Manual</td>\n",
 232 |        "      <td>0</td>\n",
 233 |        "      <td>3</td>\n",
 234 |        "    </tr>\n",
 235 |        "    <tr>\n",
 236 |        "      <th>3</th>\n",
 237 |        "      <td>wagon r</td>\n",
 238 |        "      <td>2011</td>\n",
 239 |        "      <td>2.85</td>\n",
 240 |        "      <td>4.15</td>\n",
 241 |        "      <td>5200</td>\n",
 242 |        "      <td>Petrol</td>\n",
 243 |        "      <td>Dealer</td>\n",
 244 |        "      <td>Manual</td>\n",
 245 |        "      <td>0</td>\n",
 246 |        "      <td>9</td>\n",
 247 |        "    </tr>\n",
 248 |        "    <tr>\n",
 249 |        "      <th>4</th>\n",
 250 |        "      <td>swift</td>\n",
 251 |        "      <td>2014</td>\n",
 252 |        "      <td>4.60</td>\n",
 253 |        "      <td>6.87</td>\n",
 254 |        "      <td>42450</td>\n",
 255 |        "      <td>Diesel</td>\n",
 256 |        "      <td>Dealer</td>\n",
 257 |        "      <td>Manual</td>\n",
 258 |        "      <td>0</td>\n",
 259 |        "      <td>6</td>\n",
 260 |        "    </tr>\n",
 261 |        "  </tbody>\n",
 262 |        "</table>\n",
 263 |        "</div>"
 264 |       ],
 265 |       "text/plain": [
 266 |        "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
 267 |        "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
 268 |        "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
 269 |        "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
 270 |        "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
 271 |        "4    swift  2014           4.60           6.87       42450    Diesel   \n",
 272 |        "\n",
 273 |        "  Seller_Type Transmission  Owner  no_year  \n",
 274 |        "0      Dealer       Manual      0        6  \n",
 275 |        "1      Dealer       Manual      0        7  \n",
 276 |        "2      Dealer       Manual      0        3  \n",
 277 |        "3      Dealer       Manual      0        9  \n",
 278 |        "4      Dealer       Manual      0        6  "
 279 |       ]
 280 |      },
 281 |      "execution_count": 5,
 282 |      "metadata": {},
 283 |      "output_type": "execute_result"
 284 |     }
 285 |    ],
 286 |    "source": [
 287 |     "df.head()"
 288 |    ]
 289 |   },
 290 |   {
 291 |    "cell_type": "code",
 292 |    "execution_count": 6,
 293 |    "metadata": {},
 294 |    "outputs": [],
 295 |    "source": [
 296 |     "df.drop(['Car_Name','Year'],axis=1,inplace=True)"
 297 |    ]
 298 |   },
 299 |   {
 300 |    "cell_type": "code",
 301 |    "execution_count": 7,
 302 |    "metadata": {},
 303 |    "outputs": [
 304 |     {
 305 |      "data": {
 306 |       "text/html": [
 307 |        "<div>\n",
 308 |        "<style scoped>\n",
 309 |        "    .dataframe tbody tr th:only-of-type {\n",
 310 |        "        vertical-align: middle;\n",
 311 |        "    }\n",
 312 |        "\n",
 313 |        "    .dataframe tbody tr th {\n",
 314 |        "        vertical-align: top;\n",
 315 |        "    }\n",
 316 |        "\n",
 317 |        "    .dataframe thead th {\n",
 318 |        "        text-align: right;\n",
 319 |        "    }\n",
 320 |        "</style>\n",
 321 |        "<table border=\"1\" class=\"dataframe\">\n",
 322 |        "  <thead>\n",
 323 |        "    <tr style=\"text-align: right;\">\n",
 324 |        "      <th></th>\n",
 325 |        "      <th>Selling_Price</th>\n",
 326 |        "      <th>Present_Price</th>\n",
 327 |        "      <th>Kms_Driven</th>\n",
 328 |        "      <th>Fuel_Type</th>\n",
 329 |        "      <th>Seller_Type</th>\n",
 330 |        "      <th>Transmission</th>\n",
 331 |        "      <th>Owner</th>\n",
 332 |        "      <th>no_year</th>\n",
 333 |        "    </tr>\n",
 334 |        "  </thead>\n",
 335 |        "  <tbody>\n",
 336 |        "    <tr>\n",
 337 |        "      <th>0</th>\n",
 338 |        "      <td>3.35</td>\n",
 339 |        "      <td>5.59</td>\n",
 340 |        "      <td>27000</td>\n",
 341 |        "      <td>Petrol</td>\n",
 342 |        "      <td>Dealer</td>\n",
 343 |        "      <td>Manual</td>\n",
 344 |        "      <td>0</td>\n",
 345 |        "      <td>6</td>\n",
 346 |        "    </tr>\n",
 347 |        "    <tr>\n",
 348 |        "      <th>1</th>\n",
 349 |        "      <td>4.75</td>\n",
 350 |        "      <td>9.54</td>\n",
 351 |        "      <td>43000</td>\n",
 352 |        "      <td>Diesel</td>\n",
 353 |        "      <td>Dealer</td>\n",
 354 |        "      <td>Manual</td>\n",
 355 |        "      <td>0</td>\n",
 356 |        "      <td>7</td>\n",
 357 |        "    </tr>\n",
 358 |        "    <tr>\n",
 359 |        "      <th>2</th>\n",
 360 |        "      <td>7.25</td>\n",
 361 |        "      <td>9.85</td>\n",
 362 |        "      <td>6900</td>\n",
 363 |        "      <td>Petrol</td>\n",
 364 |        "      <td>Dealer</td>\n",
 365 |        "      <td>Manual</td>\n",
 366 |        "      <td>0</td>\n",
 367 |        "      <td>3</td>\n",
 368 |        "    </tr>\n",
 369 |        "    <tr>\n",
 370 |        "      <th>3</th>\n",
 371 |        "      <td>2.85</td>\n",
 372 |        "      <td>4.15</td>\n",
 373 |        "      <td>5200</td>\n",
 374 |        "      <td>Petrol</td>\n",
 375 |        "      <td>Dealer</td>\n",
 376 |        "      <td>Manual</td>\n",
 377 |        "      <td>0</td>\n",
 378 |        "      <td>9</td>\n",
 379 |        "    </tr>\n",
 380 |        "    <tr>\n",
 381 |        "      <th>4</th>\n",
 382 |        "      <td>4.60</td>\n",
 383 |        "      <td>6.87</td>\n",
 384 |        "      <td>42450</td>\n",
 385 |        "      <td>Diesel</td>\n",
 386 |        "      <td>Dealer</td>\n",
 387 |        "      <td>Manual</td>\n",
 388 |        "      <td>0</td>\n",
 389 |        "      <td>6</td>\n",
 390 |        "    </tr>\n",
 391 |        "  </tbody>\n",
 392 |        "</table>\n",
 393 |        "</div>"
 394 |       ],
 395 |       "text/plain": [
 396 |        "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
 397 |        "0           3.35           5.59       27000    Petrol      Dealer   \n",
 398 |        "1           4.75           9.54       43000    Diesel      Dealer   \n",
 399 |        "2           7.25           9.85        6900    Petrol      Dealer   \n",
 400 |        "3           2.85           4.15        5200    Petrol      Dealer   \n",
 401 |        "4           4.60           6.87       42450    Diesel      Dealer   \n",
 402 |        "\n",
 403 |        "  Transmission  Owner  no_year  \n",
 404 |        "0       Manual      0        6  \n",
 405 |        "1       Manual      0        7  \n",
 406 |        "2       Manual      0        3  \n",
 407 |        "3       Manual      0        9  \n",
 408 |        "4       Manual      0        6  "
 409 |       ]
 410 |      },
 411 |      "execution_count": 7,
 412 |      "metadata": {},
 413 |      "output_type": "execute_result"
 414 |     }
 415 |    ],
 416 |    "source": [
 417 |     "df.head()"
 418 |    ]
 419 |   },
 420 |   {
 421 |    "cell_type": "code",
 422 |    "execution_count": 8,
 423 |    "metadata": {},
 424 |    "outputs": [],
 425 |    "source": [
 426 |     "df=pd.get_dummies(df)"
 427 |    ]
 428 |   },
 429 |   {
 430 |    "cell_type": "code",
 431 |    "execution_count": 9,
 432 |    "metadata": {},
 433 |    "outputs": [
 434 |     {
 435 |      "data": {
 436 |       "text/html": [
 437 |        "<div>\n",
 438 |        "<style scoped>\n",
 439 |        "    .dataframe tbody tr th:only-of-type {\n",
 440 |        "        vertical-align: middle;\n",
 441 |        "    }\n",
 442 |        "\n",
 443 |        "    .dataframe tbody tr th {\n",
 444 |        "        vertical-align: top;\n",
 445 |        "    }\n",
 446 |        "\n",
 447 |        "    .dataframe thead th {\n",
 448 |        "        text-align: right;\n",
 449 |        "    }\n",
 450 |        "</style>\n",
 451 |        "<table border=\"1\" class=\"dataframe\">\n",
 452 |        "  <thead>\n",
 453 |        "    <tr style=\"text-align: right;\">\n",
 454 |        "      <th></th>\n",
 455 |        "      <th>Selling_Price</th>\n",
 456 |        "      <th>Present_Price</th>\n",
 457 |        "      <th>Kms_Driven</th>\n",
 458 |        "      <th>Owner</th>\n",
 459 |        "      <th>no_year</th>\n",
 460 |        "      <th>Fuel_Type_CNG</th>\n",
 461 |        "      <th>Fuel_Type_Diesel</th>\n",
 462 |        "      <th>Fuel_Type_Petrol</th>\n",
 463 |        "      <th>Seller_Type_Dealer</th>\n",
 464 |        "      <th>Seller_Type_Individual</th>\n",
 465 |        "      <th>Transmission_Automatic</th>\n",
 466 |        "      <th>Transmission_Manual</th>\n",
 467 |        "    </tr>\n",
 468 |        "  </thead>\n",
 469 |        "  <tbody>\n",
 470 |        "    <tr>\n",
 471 |        "      <th>0</th>\n",
 472 |        "      <td>3.35</td>\n",
 473 |        "      <td>5.59</td>\n",
 474 |        "      <td>27000</td>\n",
 475 |        "      <td>0</td>\n",
 476 |        "      <td>6</td>\n",
 477 |        "      <td>0</td>\n",
 478 |        "      <td>0</td>\n",
 479 |        "      <td>1</td>\n",
 480 |        "      <td>1</td>\n",
 481 |        "      <td>0</td>\n",
 482 |        "      <td>0</td>\n",
 483 |        "      <td>1</td>\n",
 484 |        "    </tr>\n",
 485 |        "    <tr>\n",
 486 |        "      <th>1</th>\n",
 487 |        "      <td>4.75</td>\n",
 488 |        "      <td>9.54</td>\n",
 489 |        "      <td>43000</td>\n",
 490 |        "      <td>0</td>\n",
 491 |        "      <td>7</td>\n",
 492 |        "      <td>0</td>\n",
 493 |        "      <td>1</td>\n",
 494 |        "      <td>0</td>\n",
 495 |        "      <td>1</td>\n",
 496 |        "      <td>0</td>\n",
 497 |        "      <td>0</td>\n",
 498 |        "      <td>1</td>\n",
 499 |        "    </tr>\n",
 500 |        "    <tr>\n",
 501 |        "      <th>2</th>\n",
 502 |        "      <td>7.25</td>\n",
 503 |        "      <td>9.85</td>\n",
 504 |        "      <td>6900</td>\n",
 505 |        "      <td>0</td>\n",
 506 |        "      <td>3</td>\n",
 507 |        "      <td>0</td>\n",
 508 |        "      <td>0</td>\n",
 509 |        "      <td>1</td>\n",
 510 |        "      <td>1</td>\n",
 511 |        "      <td>0</td>\n",
 512 |        "      <td>0</td>\n",
 513 |        "      <td>1</td>\n",
 514 |        "    </tr>\n",
 515 |        "    <tr>\n",
 516 |        "      <th>3</th>\n",
 517 |        "      <td>2.85</td>\n",
 518 |        "      <td>4.15</td>\n",
 519 |        "      <td>5200</td>\n",
 520 |        "      <td>0</td>\n",
 521 |        "      <td>9</td>\n",
 522 |        "      <td>0</td>\n",
 523 |        "      <td>0</td>\n",
 524 |        "      <td>1</td>\n",
 525 |        "      <td>1</td>\n",
 526 |        "      <td>0</td>\n",
 527 |        "      <td>0</td>\n",
 528 |        "      <td>1</td>\n",
 529 |        "    </tr>\n",
 530 |        "    <tr>\n",
 531 |        "      <th>4</th>\n",
 532 |        "      <td>4.60</td>\n",
 533 |        "      <td>6.87</td>\n",
 534 |        "      <td>42450</td>\n",
 535 |        "      <td>0</td>\n",
 536 |        "      <td>6</td>\n",
 537 |        "      <td>0</td>\n",
 538 |        "      <td>1</td>\n",
 539 |        "      <td>0</td>\n",
 540 |        "      <td>1</td>\n",
 541 |        "      <td>0</td>\n",
 542 |        "      <td>0</td>\n",
 543 |        "      <td>1</td>\n",
 544 |        "    </tr>\n",
 545 |        "  </tbody>\n",
 546 |        "</table>\n",
 547 |        "</div>"
 548 |       ],
 549 |       "text/plain": [
 550 |        "   Selling_Price  Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_CNG  \\\n",
 551 |        "0           3.35           5.59       27000      0        6              0   \n",
 552 |        "1           4.75           9.54       43000      0        7              0   \n",
 553 |        "2           7.25           9.85        6900      0        3              0   \n",
 554 |        "3           2.85           4.15        5200      0        9              0   \n",
 555 |        "4           4.60           6.87       42450      0        6              0   \n",
 556 |        "\n",
 557 |        "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Dealer  \\\n",
 558 |        "0                 0                 1                   1   \n",
 559 |        "1                 1                 0                   1   \n",
 560 |        "2                 0                 1                   1   \n",
 561 |        "3                 0                 1                   1   \n",
 562 |        "4                 1                 0                   1   \n",
 563 |        "\n",
 564 |        "   Seller_Type_Individual  Transmission_Automatic  Transmission_Manual  \n",
 565 |        "0                       0                       0                    1  \n",
 566 |        "1                       0                       0                    1  \n",
 567 |        "2                       0                       0                    1  \n",
 568 |        "3                       0                       0                    1  \n",
 569 |        "4                       0                       0                    1  "
 570 |       ]
 571 |      },
 572 |      "execution_count": 9,
 573 |      "metadata": {},
 574 |      "output_type": "execute_result"
 575 |     }
 576 |    ],
 577 |    "source": [
 578 |     "df.head()"
 579 |    ]
 580 |   },
 581 |   {
 582 |    "cell_type": "code",
 583 |    "execution_count": 10,
 584 |    "metadata": {},
 585 |    "outputs": [],
 586 |    "source": [
 587 |     "x=df.drop('Selling_Price',axis=1)\n",
 588 |     "y=df['Selling_Price']"
 589 |    ]
 590 |   },
 591 |   {
 592 |    "cell_type": "code",
 593 |    "execution_count": 11,
 594 |    "metadata": {},
 595 |    "outputs": [
 596 |     {
 597 |      "data": {
 598 |       "text/html": [
 599 |        "<div>\n",
 600 |        "<style scoped>\n",
 601 |        "    .dataframe tbody tr th:only-of-type {\n",
 602 |        "        vertical-align: middle;\n",
 603 |        "    }\n",
 604 |        "\n",
 605 |        "    .dataframe tbody tr th {\n",
 606 |        "        vertical-align: top;\n",
 607 |        "    }\n",
 608 |        "\n",
 609 |        "    .dataframe thead th {\n",
 610 |        "        text-align: right;\n",
 611 |        "    }\n",
 612 |        "</style>\n",
 613 |        "<table border=\"1\" class=\"dataframe\">\n",
 614 |        "  <thead>\n",
 615 |        "    <tr style=\"text-align: right;\">\n",
 616 |        "      <th></th>\n",
 617 |        "      <th>Present_Price</th>\n",
 618 |        "      <th>Kms_Driven</th>\n",
 619 |        "      <th>Owner</th>\n",
 620 |        "      <th>no_year</th>\n",
 621 |        "      <th>Fuel_Type_CNG</th>\n",
 622 |        "      <th>Fuel_Type_Diesel</th>\n",
 623 |        "      <th>Fuel_Type_Petrol</th>\n",
 624 |        "      <th>Seller_Type_Dealer</th>\n",
 625 |        "      <th>Seller_Type_Individual</th>\n",
 626 |        "      <th>Transmission_Automatic</th>\n",
 627 |        "      <th>Transmission_Manual</th>\n",
 628 |        "    </tr>\n",
 629 |        "  </thead>\n",
 630 |        "  <tbody>\n",
 631 |        "    <tr>\n",
 632 |        "      <th>0</th>\n",
 633 |        "      <td>5.59</td>\n",
 634 |        "      <td>27000</td>\n",
 635 |        "      <td>0</td>\n",
 636 |        "      <td>6</td>\n",
 637 |        "      <td>0</td>\n",
 638 |        "      <td>0</td>\n",
 639 |        "      <td>1</td>\n",
 640 |        "      <td>1</td>\n",
 641 |        "      <td>0</td>\n",
 642 |        "      <td>0</td>\n",
 643 |        "      <td>1</td>\n",
 644 |        "    </tr>\n",
 645 |        "    <tr>\n",
 646 |        "      <th>1</th>\n",
 647 |        "      <td>9.54</td>\n",
 648 |        "      <td>43000</td>\n",
 649 |        "      <td>0</td>\n",
 650 |        "      <td>7</td>\n",
 651 |        "      <td>0</td>\n",
 652 |        "      <td>1</td>\n",
 653 |        "      <td>0</td>\n",
 654 |        "      <td>1</td>\n",
 655 |        "      <td>0</td>\n",
 656 |        "      <td>0</td>\n",
 657 |        "      <td>1</td>\n",
 658 |        "    </tr>\n",
 659 |        "    <tr>\n",
 660 |        "      <th>2</th>\n",
 661 |        "      <td>9.85</td>\n",
 662 |        "      <td>6900</td>\n",
 663 |        "      <td>0</td>\n",
 664 |        "      <td>3</td>\n",
 665 |        "      <td>0</td>\n",
 666 |        "      <td>0</td>\n",
 667 |        "      <td>1</td>\n",
 668 |        "      <td>1</td>\n",
 669 |        "      <td>0</td>\n",
 670 |        "      <td>0</td>\n",
 671 |        "      <td>1</td>\n",
 672 |        "    </tr>\n",
 673 |        "    <tr>\n",
 674 |        "      <th>3</th>\n",
 675 |        "      <td>4.15</td>\n",
 676 |        "      <td>5200</td>\n",
 677 |        "      <td>0</td>\n",
 678 |        "      <td>9</td>\n",
 679 |        "      <td>0</td>\n",
 680 |        "      <td>0</td>\n",
 681 |        "      <td>1</td>\n",
 682 |        "      <td>1</td>\n",
 683 |        "      <td>0</td>\n",
 684 |        "      <td>0</td>\n",
 685 |        "      <td>1</td>\n",
 686 |        "    </tr>\n",
 687 |        "    <tr>\n",
 688 |        "      <th>4</th>\n",
 689 |        "      <td>6.87</td>\n",
 690 |        "      <td>42450</td>\n",
 691 |        "      <td>0</td>\n",
 692 |        "      <td>6</td>\n",
 693 |        "      <td>0</td>\n",
 694 |        "      <td>1</td>\n",
 695 |        "      <td>0</td>\n",
 696 |        "      <td>1</td>\n",
 697 |        "      <td>0</td>\n",
 698 |        "      <td>0</td>\n",
 699 |        "      <td>1</td>\n",
 700 |        "    </tr>\n",
 701 |        "  </tbody>\n",
 702 |        "</table>\n",
 703 |        "</div>"
 704 |       ],
 705 |       "text/plain": [
 706 |        "   Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_CNG  Fuel_Type_Diesel  \\\n",
 707 |        "0           5.59       27000      0        6              0                 0   \n",
 708 |        "1           9.54       43000      0        7              0                 1   \n",
 709 |        "2           9.85        6900      0        3              0                 0   \n",
 710 |        "3           4.15        5200      0        9              0                 0   \n",
 711 |        "4           6.87       42450      0        6              0                 1   \n",
 712 |        "\n",
 713 |        "   Fuel_Type_Petrol  Seller_Type_Dealer  Seller_Type_Individual  \\\n",
 714 |        "0                 1                   1                       0   \n",
 715 |        "1                 0                   1                       0   \n",
 716 |        "2                 1                   1                       0   \n",
 717 |        "3                 1                   1                       0   \n",
 718 |        "4                 0                   1                       0   \n",
 719 |        "\n",
 720 |        "   Transmission_Automatic  Transmission_Manual  \n",
 721 |        "0                       0                    1  \n",
 722 |        "1                       0                    1  \n",
 723 |        "2                       0                    1  \n",
 724 |        "3                       0                    1  \n",
 725 |        "4                       0                    1  "
 726 |       ]
 727 |      },
 728 |      "execution_count": 11,
 729 |      "metadata": {},
 730 |      "output_type": "execute_result"
 731 |     }
 732 |    ],
 733 |    "source": [
 734 |     "x.head()"
 735 |    ]
 736 |   },
 737 |   {
 738 |    "cell_type": "code",
 739 |    "execution_count": 12,
 740 |    "metadata": {},
 741 |    "outputs": [
 742 |     {
 743 |      "data": {
 744 |       "text/plain": [
 745 |        "0    3.35\n",
 746 |        "1    4.75\n",
 747 |        "2    7.25\n",
 748 |        "3    2.85\n",
 749 |        "4    4.60\n",
 750 |        "Name: Selling_Price, dtype: float64"
 751 |       ]
 752 |      },
 753 |      "execution_count": 12,
 754 |      "metadata": {},
 755 |      "output_type": "execute_result"
 756 |     }
 757 |    ],
 758 |    "source": [
 759 |     "y.head()"
 760 |    ]
 761 |   },
 762 |   {
 763 |    "cell_type": "code",
 764 |    "execution_count": 13,
 765 |    "metadata": {},
 766 |    "outputs": [],
 767 |    "source": [
 768 |     "from sklearn.model_selection import train_test_split"
 769 |    ]
 770 |   },
 771 |   {
 772 |    "cell_type": "code",
 773 |    "execution_count": 14,
 774 |    "metadata": {},
 775 |    "outputs": [],
 776 |    "source": [
 777 |     "x_train, x_test, y_train, y_test = train_test_split(x,y,random_state=5)"
 778 |    ]
 779 |   },
 780 |   {
 781 |    "cell_type": "code",
 782 |    "execution_count": 15,
 783 |    "metadata": {},
 784 |    "outputs": [
 785 |     {
 786 |      "data": {
 787 |       "text/plain": [
 788 |        "((225, 11), (225,))"
 789 |       ]
 790 |      },
 791 |      "execution_count": 15,
 792 |      "metadata": {},
 793 |      "output_type": "execute_result"
 794 |     }
 795 |    ],
 796 |    "source": [
 797 |     "x_train.shape , y_train.shape"
 798 |    ]
 799 |   },
 800 |   {
 801 |    "cell_type": "code",
 802 |    "execution_count": 16,
 803 |    "metadata": {},
 804 |    "outputs": [],
 805 |    "source": [
 806 |     "from sklearn.linear_model import LinearRegression"
 807 |    ]
 808 |   },
 809 |   {
 810 |    "cell_type": "code",
 811 |    "execution_count": 17,
 812 |    "metadata": {},
 813 |    "outputs": [
 814 |     {
 815 |      "data": {
 816 |       "text/plain": [
 817 |        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
 818 |       ]
 819 |      },
 820 |      "execution_count": 17,
 821 |      "metadata": {},
 822 |      "output_type": "execute_result"
 823 |     }
 824 |    ],
 825 |    "source": [
 826 |     "lr=LinearRegression()\n",
 827 |     "lr.fit(x_train,y_train)"
 828 |    ]
 829 |   },
 830 |   {
 831 |    "cell_type": "code",
 832 |    "execution_count": 18,
 833 |    "metadata": {},
 834 |    "outputs": [],
 835 |    "source": [
 836 |     "prediction = lr.predict(x_test)"
 837 |    ]
 838 |   },
 839 |   {
 840 |    "cell_type": "code",
 841 |    "execution_count": 19,
 842 |    "metadata": {},
 843 |    "outputs": [],
 844 |    "source": [
 845 |     "import matplotlib.pyplot as plt\n",
 846 |     "import seaborn as sns"
 847 |    ]
 848 |   },
 849 |   {
 850 |    "cell_type": "code",
 851 |    "execution_count": 20,
 852 |    "metadata": {},
 853 |    "outputs": [
 854 |     {
 855 |      "data": {
 856 |       "text/plain": [
 857 |        "<matplotlib.collections.PathCollection at 0x1b09bba2c48>"
 858 |       ]
 859 |      },
 860 |      "execution_count": 20,
 861 |      "metadata": {},
 862 |      "output_type": "execute_result"
 863 |     },
 864 |     {
 865 |      "data": {
 866 |       "image/png": "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\n",
 867 |       "text/plain": [
 868 |        "<Figure size 432x288 with 1 Axes>"
 869 |       ]
 870 |      },
 871 |      "metadata": {
 872 |       "needs_background": "light"
 873 |      },
 874 |      "output_type": "display_data"
 875 |     }
 876 |    ],
 877 |    "source": [
 878 |     "plt.scatter(y_test,prediction)"
 879 |    ]
 880 |   },
 881 |   {
 882 |    "cell_type": "code",
 883 |    "execution_count": 21,
 884 |    "metadata": {},
 885 |    "outputs": [
 886 |     {
 887 |      "data": {
 888 |       "text/plain": [
 889 |        "<matplotlib.axes._subplots.AxesSubplot at 0x1b09d333908>"
 890 |       ]
 891 |      },
 892 |      "execution_count": 21,
 893 |      "metadata": {},
 894 |      "output_type": "execute_result"
 895 |     },
 896 |     {
 897 |      "data": {
 898 |       "image/png": 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\n",
 899 |       "text/plain": [
 900 |        "<Figure size 432x288 with 1 Axes>"
 901 |       ]
 902 |      },
 903 |      "metadata": {
 904 |       "needs_background": "light"
 905 |      },
 906 |      "output_type": "display_data"
 907 |     }
 908 |    ],
 909 |    "source": [
 910 |     "sns.distplot(y_test-prediction)"
 911 |    ]
 912 |   },
 913 |   {
 914 |    "cell_type": "code",
 915 |    "execution_count": 22,
 916 |    "metadata": {},
 917 |    "outputs": [],
 918 |    "source": [
 919 |     "from sklearn import metrics\n",
 920 |     "import numpy as np"
 921 |    ]
 922 |   },
 923 |   {
 924 |    "cell_type": "code",
 925 |    "execution_count": 23,
 926 |    "metadata": {},
 927 |    "outputs": [
 928 |     {
 929 |      "name": "stdout",
 930 |      "output_type": "stream",
 931 |      "text": [
 932 |       "MAE:  1.140996108785903\n",
 933 |       "MSE:  3.2815963416575036\n",
 934 |       "RMSE:  1.8115176901309862\n",
 935 |       "R squared:  0.8608525898499443\n"
 936 |      ]
 937 |     }
 938 |    ],
 939 |    "source": [
 940 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
 941 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
 942 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
 943 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
 944 |    ]
 945 |   },
 946 |   {
 947 |    "cell_type": "code",
 948 |    "execution_count": 25,
 949 |    "metadata": {},
 950 |    "outputs": [],
 951 |    "source": [
 952 |     "from sklearn.linear_model import Ridge"
 953 |    ]
 954 |   },
 955 |   {
 956 |    "cell_type": "code",
 957 |    "execution_count": 26,
 958 |    "metadata": {},
 959 |    "outputs": [],
 960 |    "source": [
 961 |     "ridge=Ridge()\n",
 962 |     "ridge.fit(x_train,y_train)\n",
 963 |     "prediction = ridge.predict(x_test)"
 964 |    ]
 965 |   },
 966 |   {
 967 |    "cell_type": "code",
 968 |    "execution_count": 27,
 969 |    "metadata": {},
 970 |    "outputs": [
 971 |     {
 972 |      "name": "stdout",
 973 |      "output_type": "stream",
 974 |      "text": [
 975 |       "MAE:  1.1332339163458571\n",
 976 |       "MSE:  3.2738266436526455\n",
 977 |       "RMSE:  1.8093718920256956\n",
 978 |       "R squared:  0.8611820433361332\n"
 979 |      ]
 980 |     }
 981 |    ],
 982 |    "source": [
 983 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
 984 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
 985 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
 986 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
 987 |    ]
 988 |   },
 989 |   {
 990 |    "cell_type": "code",
 991 |    "execution_count": 28,
 992 |    "metadata": {},
 993 |    "outputs": [],
 994 |    "source": [
 995 |     "from sklearn.linear_model import Lasso"
 996 |    ]
 997 |   },
 998 |   {
 999 |    "cell_type": "code",
1000 |    "execution_count": 29,
1001 |    "metadata": {},
1002 |    "outputs": [
1003 |     {
1004 |      "name": "stdout",
1005 |      "output_type": "stream",
1006 |      "text": [
1007 |       "MAE:  1.3349151159008392\n",
1008 |       "MSE:  4.47313681445178\n",
1009 |       "RMSE:  2.1149791522499175\n",
1010 |       "R squared:  0.8103284687770421\n"
1011 |      ]
1012 |     }
1013 |    ],
1014 |    "source": [
1015 |     "ridge=Lasso()\n",
1016 |     "ridge.fit(x_train,y_train)\n",
1017 |     "prediction = ridge.predict(x_test)\n",
1018 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
1019 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
1020 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
1021 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
1022 |    ]
1023 |   },
1024 |   {
1025 |    "cell_type": "code",
1026 |    "execution_count": 30,
1027 |    "metadata": {},
1028 |    "outputs": [],
1029 |    "source": [
1030 |     "from sklearn.model_selection import RandomizedSearchCV,GridSearchCV"
1031 |    ]
1032 |   },
1033 |   {
1034 |    "cell_type": "code",
1035 |    "execution_count": 31,
1036 |    "metadata": {},
1037 |    "outputs": [
1038 |     {
1039 |      "name": "stdout",
1040 |      "output_type": "stream",
1041 |      "text": [
1042 |       "MAE:  1.1187640189415795\n",
1043 |       "MSE:  3.2349381933942647\n",
1044 |       "RMSE:  1.798593393014181\n",
1045 |       "R squared:  0.8628310051750747\n"
1046 |      ]
1047 |     },
1048 |     {
1049 |      "name": "stderr",
1050 |      "output_type": "stream",
1051 |      "text": [
1052 |       "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:281: UserWarning: The total space of parameters 2 is smaller than n_iter=10. Running 2 iterations. For exhaustive searches, use GridSearchCV.\n",
1053 |       "  % (grid_size, self.n_iter, grid_size), UserWarning)\n"
1054 |      ]
1055 |     }
1056 |    ],
1057 |    "source": [
1058 |     "r=Ridge()\n",
1059 |     "parameters={'alpha':[1,20]}\n",
1060 |     "l=RandomizedSearchCV(r,parameters,scoring=\"neg_mean_squared_error\",cv=5)\n",
1061 |     "l.fit(x_train,y_train)\n",
1062 |     "prediction = l.predict(x_test)\n",
1063 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
1064 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
1065 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
1066 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
1067 |    ]
1068 |   },
1069 |   {
1070 |    "cell_type": "code",
1071 |    "execution_count": 32,
1072 |    "metadata": {},
1073 |    "outputs": [
1074 |     {
1075 |      "name": "stdout",
1076 |      "output_type": "stream",
1077 |      "text": [
1078 |       "MAE:  1.1194605545945522\n",
1079 |       "MSE:  3.235117272776871\n",
1080 |       "RMSE:  1.798643175501153\n",
1081 |       "R squared:  0.8628234117876782\n"
1082 |      ]
1083 |     }
1084 |    ],
1085 |    "source": [
1086 |     "r=Ridge()\n",
1087 |     "parameters={'alpha':[1000,100,30,40,50]}\n",
1088 |     "l=GridSearchCV(r,parameters,scoring=\"neg_mean_squared_error\",cv=5)\n",
1089 |     "l.fit(x_train,y_train)\n",
1090 |     "prediction = l.predict(x_test)\n",
1091 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
1092 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
1093 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
1094 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
1095 |    ]
1096 |   },
1097 |   {
1098 |    "cell_type": "code",
1099 |    "execution_count": 33,
1100 |    "metadata": {},
1101 |    "outputs": [
1102 |     {
1103 |      "data": {
1104 |       "text/plain": [
1105 |        "{'alpha': 30}"
1106 |       ]
1107 |      },
1108 |      "execution_count": 33,
1109 |      "metadata": {},
1110 |      "output_type": "execute_result"
1111 |     }
1112 |    ],
1113 |    "source": [
1114 |     "l.best_params_"
1115 |    ]
1116 |   },
1117 |   {
1118 |    "cell_type": "code",
1119 |    "execution_count": 24,
1120 |    "metadata": {},
1121 |    "outputs": [],
1122 |    "source": [
1123 |     "from sklearn.ensemble import RandomForestRegressor"
1124 |    ]
1125 |   },
1126 |   {
1127 |    "cell_type": "code",
1128 |    "execution_count": 35,
1129 |    "metadata": {},
1130 |    "outputs": [],
1131 |    "source": [
1132 |     "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
1133 |     "# Number of features to consider at every split\n",
1134 |     "max_features = ['auto', 'sqrt']\n",
1135 |     "# Maximum number of levels in tree\n",
1136 |     "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
1137 |     "max_depth.append(None)\n",
1138 |     "# Minimum number of samples required to split a node\n",
1139 |     "min_samples_split = [2, 5, 10]\n",
1140 |     "# Minimum number of samples required at each leaf node\n",
1141 |     "min_samples_leaf = [1, 2, 4]\n",
1142 |     "# Method of selecting samples for training each tree\n",
1143 |     "bootstrap = [True, False]\n",
1144 |     "# Create the random grid\n",
1145 |     "random_grid = {'n_estimators': n_estimators,\n",
1146 |     "               'max_features': max_features,\n",
1147 |     "               'max_depth': max_depth,\n",
1148 |     "               'min_samples_split': min_samples_split,\n",
1149 |     "               'min_samples_leaf': min_samples_leaf,\n",
1150 |     "               'bootstrap': bootstrap}"
1151 |    ]
1152 |   },
1153 |   {
1154 |    "cell_type": "code",
1155 |    "execution_count": 49,
1156 |    "metadata": {},
1157 |    "outputs": [
1158 |     {
1159 |      "name": "stdout",
1160 |      "output_type": "stream",
1161 |      "text": [
1162 |       "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
1163 |      ]
1164 |     },
1165 |     {
1166 |      "name": "stderr",
1167 |      "output_type": "stream",
1168 |      "text": [
1169 |       "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
1170 |       "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   35.2s\n",
1171 |       "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  2.7min\n",
1172 |       "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  5.0min finished\n"
1173 |      ]
1174 |     },
1175 |     {
1176 |      "name": "stdout",
1177 |      "output_type": "stream",
1178 |      "text": [
1179 |       "MAE:  0.565409210526331\n",
1180 |       "MSE:  1.071250076830035\n",
1181 |       "RMSE:  1.0350121143397477\n",
1182 |       "R squared:  0.9545764748937227\n"
1183 |      ]
1184 |     }
1185 |    ],
1186 |    "source": [
1187 |     "r=RandomForestRegressor(n_estimators=100)\n",
1188 |     "rf = RandomForestRegressor()\n",
1189 |     "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
1190 |     "rf_random.fit(x_train,y_train)\n",
1191 |     "prediction = rf_random.predict(x_test)\n",
1192 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
1193 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
1194 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
1195 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
1196 |    ]
1197 |   },
1198 |   {
1199 |    "cell_type": "code",
1200 |    "execution_count": 36,
1201 |    "metadata": {},
1202 |    "outputs": [
1203 |     {
1204 |      "name": "stdout",
1205 |      "output_type": "stream",
1206 |      "text": [
1207 |       "MAE:  0.49281447368421116\n",
1208 |       "MSE:  0.603024184078948\n",
1209 |       "RMSE:  0.7765463180512467\n",
1210 |       "R squared:  0.9744303550051941\n"
1211 |      ]
1212 |     }
1213 |    ],
1214 |    "source": [
1215 |     "r=RandomForestRegressor(n_estimators=100)\n",
1216 |     "\n",
1217 |     "r.fit(x_train,y_train)\n",
1218 |     "prediction = r.predict(x_test)\n",
1219 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
1220 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
1221 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
1222 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
1223 |    ]
1224 |   },
1225 |   {
1226 |    "cell_type": "code",
1227 |    "execution_count": 38,
1228 |    "metadata": {},
1229 |    "outputs": [],
1230 |    "source": [
1231 |     "import pickle \n",
1232 |     "pickle.dump(r,open('model.pkl','wb'))"
1233 |    ]
1234 |   },
1235 |   {
1236 |    "cell_type": "code",
1237 |    "execution_count": null,
1238 |    "metadata": {},
1239 |    "outputs": [],
1240 |    "source": []
1241 |   }
1242 |  ],
1243 |  "metadata": {
1244 |   "kernelspec": {
1245 |    "display_name": "Python 3",
1246 |    "language": "python",
1247 |    "name": "python3"
1248 |   },
1249 |   "language_info": {
1250 |    "codemirror_mode": {
1251 |     "name": "ipython",
1252 |     "version": 3
1253 |    },
1254 |    "file_extension": ".py",
1255 |    "mimetype": "text/x-python",
1256 |    "name": "python",
1257 |    "nbconvert_exporter": "python",
1258 |    "pygments_lexer": "ipython3",
1259 |    "version": "3.7.6"
1260 |   }
1261 |  },
1262 |  "nbformat": 4,
1263 |  "nbformat_minor": 4
1264 | }
1265 | 


--------------------------------------------------------------------------------
/Data_preprocessing.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "import pandas as pd"
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "code",
  14 |    "execution_count": 2,
  15 |    "metadata": {},
  16 |    "outputs": [],
  17 |    "source": [
  18 |     "df=pd.read_csv('car data.csv')"
  19 |    ]
  20 |   },
  21 |   {
  22 |    "cell_type": "code",
  23 |    "execution_count": 3,
  24 |    "metadata": {},
  25 |    "outputs": [
  26 |     {
  27 |      "data": {
  28 |       "text/html": [
  29 |        "<div>\n",
  30 |        "<style scoped>\n",
  31 |        "    .dataframe tbody tr th:only-of-type {\n",
  32 |        "        vertical-align: middle;\n",
  33 |        "    }\n",
  34 |        "\n",
  35 |        "    .dataframe tbody tr th {\n",
  36 |        "        vertical-align: top;\n",
  37 |        "    }\n",
  38 |        "\n",
  39 |        "    .dataframe thead th {\n",
  40 |        "        text-align: right;\n",
  41 |        "    }\n",
  42 |        "</style>\n",
  43 |        "<table border=\"1\" class=\"dataframe\">\n",
  44 |        "  <thead>\n",
  45 |        "    <tr style=\"text-align: right;\">\n",
  46 |        "      <th></th>\n",
  47 |        "      <th>Car_Name</th>\n",
  48 |        "      <th>Year</th>\n",
  49 |        "      <th>Selling_Price</th>\n",
  50 |        "      <th>Present_Price</th>\n",
  51 |        "      <th>Kms_Driven</th>\n",
  52 |        "      <th>Fuel_Type</th>\n",
  53 |        "      <th>Seller_Type</th>\n",
  54 |        "      <th>Transmission</th>\n",
  55 |        "      <th>Owner</th>\n",
  56 |        "    </tr>\n",
  57 |        "  </thead>\n",
  58 |        "  <tbody>\n",
  59 |        "    <tr>\n",
  60 |        "      <th>0</th>\n",
  61 |        "      <td>ritz</td>\n",
  62 |        "      <td>2014</td>\n",
  63 |        "      <td>3.35</td>\n",
  64 |        "      <td>5.59</td>\n",
  65 |        "      <td>27000</td>\n",
  66 |        "      <td>Petrol</td>\n",
  67 |        "      <td>Dealer</td>\n",
  68 |        "      <td>Manual</td>\n",
  69 |        "      <td>0</td>\n",
  70 |        "    </tr>\n",
  71 |        "    <tr>\n",
  72 |        "      <th>1</th>\n",
  73 |        "      <td>sx4</td>\n",
  74 |        "      <td>2013</td>\n",
  75 |        "      <td>4.75</td>\n",
  76 |        "      <td>9.54</td>\n",
  77 |        "      <td>43000</td>\n",
  78 |        "      <td>Diesel</td>\n",
  79 |        "      <td>Dealer</td>\n",
  80 |        "      <td>Manual</td>\n",
  81 |        "      <td>0</td>\n",
  82 |        "    </tr>\n",
  83 |        "    <tr>\n",
  84 |        "      <th>2</th>\n",
  85 |        "      <td>ciaz</td>\n",
  86 |        "      <td>2017</td>\n",
  87 |        "      <td>7.25</td>\n",
  88 |        "      <td>9.85</td>\n",
  89 |        "      <td>6900</td>\n",
  90 |        "      <td>Petrol</td>\n",
  91 |        "      <td>Dealer</td>\n",
  92 |        "      <td>Manual</td>\n",
  93 |        "      <td>0</td>\n",
  94 |        "    </tr>\n",
  95 |        "    <tr>\n",
  96 |        "      <th>3</th>\n",
  97 |        "      <td>wagon r</td>\n",
  98 |        "      <td>2011</td>\n",
  99 |        "      <td>2.85</td>\n",
 100 |        "      <td>4.15</td>\n",
 101 |        "      <td>5200</td>\n",
 102 |        "      <td>Petrol</td>\n",
 103 |        "      <td>Dealer</td>\n",
 104 |        "      <td>Manual</td>\n",
 105 |        "      <td>0</td>\n",
 106 |        "    </tr>\n",
 107 |        "    <tr>\n",
 108 |        "      <th>4</th>\n",
 109 |        "      <td>swift</td>\n",
 110 |        "      <td>2014</td>\n",
 111 |        "      <td>4.60</td>\n",
 112 |        "      <td>6.87</td>\n",
 113 |        "      <td>42450</td>\n",
 114 |        "      <td>Diesel</td>\n",
 115 |        "      <td>Dealer</td>\n",
 116 |        "      <td>Manual</td>\n",
 117 |        "      <td>0</td>\n",
 118 |        "    </tr>\n",
 119 |        "  </tbody>\n",
 120 |        "</table>\n",
 121 |        "</div>"
 122 |       ],
 123 |       "text/plain": [
 124 |        "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
 125 |        "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
 126 |        "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
 127 |        "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
 128 |        "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
 129 |        "4    swift  2014           4.60           6.87       42450    Diesel   \n",
 130 |        "\n",
 131 |        "  Seller_Type Transmission  Owner  \n",
 132 |        "0      Dealer       Manual      0  \n",
 133 |        "1      Dealer       Manual      0  \n",
 134 |        "2      Dealer       Manual      0  \n",
 135 |        "3      Dealer       Manual      0  \n",
 136 |        "4      Dealer       Manual      0  "
 137 |       ]
 138 |      },
 139 |      "execution_count": 3,
 140 |      "metadata": {},
 141 |      "output_type": "execute_result"
 142 |     }
 143 |    ],
 144 |    "source": [
 145 |     "df.head()"
 146 |    ]
 147 |   },
 148 |   {
 149 |    "cell_type": "code",
 150 |    "execution_count": 4,
 151 |    "metadata": {},
 152 |    "outputs": [],
 153 |    "source": [
 154 |     "df['no_year']=2020-df['Year']"
 155 |    ]
 156 |   },
 157 |   {
 158 |    "cell_type": "code",
 159 |    "execution_count": 5,
 160 |    "metadata": {},
 161 |    "outputs": [
 162 |     {
 163 |      "data": {
 164 |       "text/html": [
 165 |        "<div>\n",
 166 |        "<style scoped>\n",
 167 |        "    .dataframe tbody tr th:only-of-type {\n",
 168 |        "        vertical-align: middle;\n",
 169 |        "    }\n",
 170 |        "\n",
 171 |        "    .dataframe tbody tr th {\n",
 172 |        "        vertical-align: top;\n",
 173 |        "    }\n",
 174 |        "\n",
 175 |        "    .dataframe thead th {\n",
 176 |        "        text-align: right;\n",
 177 |        "    }\n",
 178 |        "</style>\n",
 179 |        "<table border=\"1\" class=\"dataframe\">\n",
 180 |        "  <thead>\n",
 181 |        "    <tr style=\"text-align: right;\">\n",
 182 |        "      <th></th>\n",
 183 |        "      <th>Car_Name</th>\n",
 184 |        "      <th>Year</th>\n",
 185 |        "      <th>Selling_Price</th>\n",
 186 |        "      <th>Present_Price</th>\n",
 187 |        "      <th>Kms_Driven</th>\n",
 188 |        "      <th>Fuel_Type</th>\n",
 189 |        "      <th>Seller_Type</th>\n",
 190 |        "      <th>Transmission</th>\n",
 191 |        "      <th>Owner</th>\n",
 192 |        "      <th>no_year</th>\n",
 193 |        "    </tr>\n",
 194 |        "  </thead>\n",
 195 |        "  <tbody>\n",
 196 |        "    <tr>\n",
 197 |        "      <th>0</th>\n",
 198 |        "      <td>ritz</td>\n",
 199 |        "      <td>2014</td>\n",
 200 |        "      <td>3.35</td>\n",
 201 |        "      <td>5.59</td>\n",
 202 |        "      <td>27000</td>\n",
 203 |        "      <td>Petrol</td>\n",
 204 |        "      <td>Dealer</td>\n",
 205 |        "      <td>Manual</td>\n",
 206 |        "      <td>0</td>\n",
 207 |        "      <td>6</td>\n",
 208 |        "    </tr>\n",
 209 |        "    <tr>\n",
 210 |        "      <th>1</th>\n",
 211 |        "      <td>sx4</td>\n",
 212 |        "      <td>2013</td>\n",
 213 |        "      <td>4.75</td>\n",
 214 |        "      <td>9.54</td>\n",
 215 |        "      <td>43000</td>\n",
 216 |        "      <td>Diesel</td>\n",
 217 |        "      <td>Dealer</td>\n",
 218 |        "      <td>Manual</td>\n",
 219 |        "      <td>0</td>\n",
 220 |        "      <td>7</td>\n",
 221 |        "    </tr>\n",
 222 |        "    <tr>\n",
 223 |        "      <th>2</th>\n",
 224 |        "      <td>ciaz</td>\n",
 225 |        "      <td>2017</td>\n",
 226 |        "      <td>7.25</td>\n",
 227 |        "      <td>9.85</td>\n",
 228 |        "      <td>6900</td>\n",
 229 |        "      <td>Petrol</td>\n",
 230 |        "      <td>Dealer</td>\n",
 231 |        "      <td>Manual</td>\n",
 232 |        "      <td>0</td>\n",
 233 |        "      <td>3</td>\n",
 234 |        "    </tr>\n",
 235 |        "    <tr>\n",
 236 |        "      <th>3</th>\n",
 237 |        "      <td>wagon r</td>\n",
 238 |        "      <td>2011</td>\n",
 239 |        "      <td>2.85</td>\n",
 240 |        "      <td>4.15</td>\n",
 241 |        "      <td>5200</td>\n",
 242 |        "      <td>Petrol</td>\n",
 243 |        "      <td>Dealer</td>\n",
 244 |        "      <td>Manual</td>\n",
 245 |        "      <td>0</td>\n",
 246 |        "      <td>9</td>\n",
 247 |        "    </tr>\n",
 248 |        "    <tr>\n",
 249 |        "      <th>4</th>\n",
 250 |        "      <td>swift</td>\n",
 251 |        "      <td>2014</td>\n",
 252 |        "      <td>4.60</td>\n",
 253 |        "      <td>6.87</td>\n",
 254 |        "      <td>42450</td>\n",
 255 |        "      <td>Diesel</td>\n",
 256 |        "      <td>Dealer</td>\n",
 257 |        "      <td>Manual</td>\n",
 258 |        "      <td>0</td>\n",
 259 |        "      <td>6</td>\n",
 260 |        "    </tr>\n",
 261 |        "  </tbody>\n",
 262 |        "</table>\n",
 263 |        "</div>"
 264 |       ],
 265 |       "text/plain": [
 266 |        "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
 267 |        "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
 268 |        "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
 269 |        "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
 270 |        "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
 271 |        "4    swift  2014           4.60           6.87       42450    Diesel   \n",
 272 |        "\n",
 273 |        "  Seller_Type Transmission  Owner  no_year  \n",
 274 |        "0      Dealer       Manual      0        6  \n",
 275 |        "1      Dealer       Manual      0        7  \n",
 276 |        "2      Dealer       Manual      0        3  \n",
 277 |        "3      Dealer       Manual      0        9  \n",
 278 |        "4      Dealer       Manual      0        6  "
 279 |       ]
 280 |      },
 281 |      "execution_count": 5,
 282 |      "metadata": {},
 283 |      "output_type": "execute_result"
 284 |     }
 285 |    ],
 286 |    "source": [
 287 |     "df.head()"
 288 |    ]
 289 |   },
 290 |   {
 291 |    "cell_type": "code",
 292 |    "execution_count": 6,
 293 |    "metadata": {},
 294 |    "outputs": [],
 295 |    "source": [
 296 |     "df.drop(['Car_Name','Year'],axis=1,inplace=True)"
 297 |    ]
 298 |   },
 299 |   {
 300 |    "cell_type": "code",
 301 |    "execution_count": 7,
 302 |    "metadata": {},
 303 |    "outputs": [
 304 |     {
 305 |      "data": {
 306 |       "text/html": [
 307 |        "<div>\n",
 308 |        "<style scoped>\n",
 309 |        "    .dataframe tbody tr th:only-of-type {\n",
 310 |        "        vertical-align: middle;\n",
 311 |        "    }\n",
 312 |        "\n",
 313 |        "    .dataframe tbody tr th {\n",
 314 |        "        vertical-align: top;\n",
 315 |        "    }\n",
 316 |        "\n",
 317 |        "    .dataframe thead th {\n",
 318 |        "        text-align: right;\n",
 319 |        "    }\n",
 320 |        "</style>\n",
 321 |        "<table border=\"1\" class=\"dataframe\">\n",
 322 |        "  <thead>\n",
 323 |        "    <tr style=\"text-align: right;\">\n",
 324 |        "      <th></th>\n",
 325 |        "      <th>Selling_Price</th>\n",
 326 |        "      <th>Present_Price</th>\n",
 327 |        "      <th>Kms_Driven</th>\n",
 328 |        "      <th>Fuel_Type</th>\n",
 329 |        "      <th>Seller_Type</th>\n",
 330 |        "      <th>Transmission</th>\n",
 331 |        "      <th>Owner</th>\n",
 332 |        "      <th>no_year</th>\n",
 333 |        "    </tr>\n",
 334 |        "  </thead>\n",
 335 |        "  <tbody>\n",
 336 |        "    <tr>\n",
 337 |        "      <th>0</th>\n",
 338 |        "      <td>3.35</td>\n",
 339 |        "      <td>5.59</td>\n",
 340 |        "      <td>27000</td>\n",
 341 |        "      <td>Petrol</td>\n",
 342 |        "      <td>Dealer</td>\n",
 343 |        "      <td>Manual</td>\n",
 344 |        "      <td>0</td>\n",
 345 |        "      <td>6</td>\n",
 346 |        "    </tr>\n",
 347 |        "    <tr>\n",
 348 |        "      <th>1</th>\n",
 349 |        "      <td>4.75</td>\n",
 350 |        "      <td>9.54</td>\n",
 351 |        "      <td>43000</td>\n",
 352 |        "      <td>Diesel</td>\n",
 353 |        "      <td>Dealer</td>\n",
 354 |        "      <td>Manual</td>\n",
 355 |        "      <td>0</td>\n",
 356 |        "      <td>7</td>\n",
 357 |        "    </tr>\n",
 358 |        "    <tr>\n",
 359 |        "      <th>2</th>\n",
 360 |        "      <td>7.25</td>\n",
 361 |        "      <td>9.85</td>\n",
 362 |        "      <td>6900</td>\n",
 363 |        "      <td>Petrol</td>\n",
 364 |        "      <td>Dealer</td>\n",
 365 |        "      <td>Manual</td>\n",
 366 |        "      <td>0</td>\n",
 367 |        "      <td>3</td>\n",
 368 |        "    </tr>\n",
 369 |        "    <tr>\n",
 370 |        "      <th>3</th>\n",
 371 |        "      <td>2.85</td>\n",
 372 |        "      <td>4.15</td>\n",
 373 |        "      <td>5200</td>\n",
 374 |        "      <td>Petrol</td>\n",
 375 |        "      <td>Dealer</td>\n",
 376 |        "      <td>Manual</td>\n",
 377 |        "      <td>0</td>\n",
 378 |        "      <td>9</td>\n",
 379 |        "    </tr>\n",
 380 |        "    <tr>\n",
 381 |        "      <th>4</th>\n",
 382 |        "      <td>4.60</td>\n",
 383 |        "      <td>6.87</td>\n",
 384 |        "      <td>42450</td>\n",
 385 |        "      <td>Diesel</td>\n",
 386 |        "      <td>Dealer</td>\n",
 387 |        "      <td>Manual</td>\n",
 388 |        "      <td>0</td>\n",
 389 |        "      <td>6</td>\n",
 390 |        "    </tr>\n",
 391 |        "  </tbody>\n",
 392 |        "</table>\n",
 393 |        "</div>"
 394 |       ],
 395 |       "text/plain": [
 396 |        "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
 397 |        "0           3.35           5.59       27000    Petrol      Dealer   \n",
 398 |        "1           4.75           9.54       43000    Diesel      Dealer   \n",
 399 |        "2           7.25           9.85        6900    Petrol      Dealer   \n",
 400 |        "3           2.85           4.15        5200    Petrol      Dealer   \n",
 401 |        "4           4.60           6.87       42450    Diesel      Dealer   \n",
 402 |        "\n",
 403 |        "  Transmission  Owner  no_year  \n",
 404 |        "0       Manual      0        6  \n",
 405 |        "1       Manual      0        7  \n",
 406 |        "2       Manual      0        3  \n",
 407 |        "3       Manual      0        9  \n",
 408 |        "4       Manual      0        6  "
 409 |       ]
 410 |      },
 411 |      "execution_count": 7,
 412 |      "metadata": {},
 413 |      "output_type": "execute_result"
 414 |     }
 415 |    ],
 416 |    "source": [
 417 |     "df.head()"
 418 |    ]
 419 |   },
 420 |   {
 421 |    "cell_type": "code",
 422 |    "execution_count": 8,
 423 |    "metadata": {},
 424 |    "outputs": [],
 425 |    "source": [
 426 |     "df=pd.get_dummies(df)"
 427 |    ]
 428 |   },
 429 |   {
 430 |    "cell_type": "code",
 431 |    "execution_count": 9,
 432 |    "metadata": {},
 433 |    "outputs": [
 434 |     {
 435 |      "data": {
 436 |       "text/html": [
 437 |        "<div>\n",
 438 |        "<style scoped>\n",
 439 |        "    .dataframe tbody tr th:only-of-type {\n",
 440 |        "        vertical-align: middle;\n",
 441 |        "    }\n",
 442 |        "\n",
 443 |        "    .dataframe tbody tr th {\n",
 444 |        "        vertical-align: top;\n",
 445 |        "    }\n",
 446 |        "\n",
 447 |        "    .dataframe thead th {\n",
 448 |        "        text-align: right;\n",
 449 |        "    }\n",
 450 |        "</style>\n",
 451 |        "<table border=\"1\" class=\"dataframe\">\n",
 452 |        "  <thead>\n",
 453 |        "    <tr style=\"text-align: right;\">\n",
 454 |        "      <th></th>\n",
 455 |        "      <th>Selling_Price</th>\n",
 456 |        "      <th>Present_Price</th>\n",
 457 |        "      <th>Kms_Driven</th>\n",
 458 |        "      <th>Owner</th>\n",
 459 |        "      <th>no_year</th>\n",
 460 |        "      <th>Fuel_Type_CNG</th>\n",
 461 |        "      <th>Fuel_Type_Diesel</th>\n",
 462 |        "      <th>Fuel_Type_Petrol</th>\n",
 463 |        "      <th>Seller_Type_Dealer</th>\n",
 464 |        "      <th>Seller_Type_Individual</th>\n",
 465 |        "      <th>Transmission_Automatic</th>\n",
 466 |        "      <th>Transmission_Manual</th>\n",
 467 |        "    </tr>\n",
 468 |        "  </thead>\n",
 469 |        "  <tbody>\n",
 470 |        "    <tr>\n",
 471 |        "      <th>0</th>\n",
 472 |        "      <td>3.35</td>\n",
 473 |        "      <td>5.59</td>\n",
 474 |        "      <td>27000</td>\n",
 475 |        "      <td>0</td>\n",
 476 |        "      <td>6</td>\n",
 477 |        "      <td>0</td>\n",
 478 |        "      <td>0</td>\n",
 479 |        "      <td>1</td>\n",
 480 |        "      <td>1</td>\n",
 481 |        "      <td>0</td>\n",
 482 |        "      <td>0</td>\n",
 483 |        "      <td>1</td>\n",
 484 |        "    </tr>\n",
 485 |        "    <tr>\n",
 486 |        "      <th>1</th>\n",
 487 |        "      <td>4.75</td>\n",
 488 |        "      <td>9.54</td>\n",
 489 |        "      <td>43000</td>\n",
 490 |        "      <td>0</td>\n",
 491 |        "      <td>7</td>\n",
 492 |        "      <td>0</td>\n",
 493 |        "      <td>1</td>\n",
 494 |        "      <td>0</td>\n",
 495 |        "      <td>1</td>\n",
 496 |        "      <td>0</td>\n",
 497 |        "      <td>0</td>\n",
 498 |        "      <td>1</td>\n",
 499 |        "    </tr>\n",
 500 |        "    <tr>\n",
 501 |        "      <th>2</th>\n",
 502 |        "      <td>7.25</td>\n",
 503 |        "      <td>9.85</td>\n",
 504 |        "      <td>6900</td>\n",
 505 |        "      <td>0</td>\n",
 506 |        "      <td>3</td>\n",
 507 |        "      <td>0</td>\n",
 508 |        "      <td>0</td>\n",
 509 |        "      <td>1</td>\n",
 510 |        "      <td>1</td>\n",
 511 |        "      <td>0</td>\n",
 512 |        "      <td>0</td>\n",
 513 |        "      <td>1</td>\n",
 514 |        "    </tr>\n",
 515 |        "    <tr>\n",
 516 |        "      <th>3</th>\n",
 517 |        "      <td>2.85</td>\n",
 518 |        "      <td>4.15</td>\n",
 519 |        "      <td>5200</td>\n",
 520 |        "      <td>0</td>\n",
 521 |        "      <td>9</td>\n",
 522 |        "      <td>0</td>\n",
 523 |        "      <td>0</td>\n",
 524 |        "      <td>1</td>\n",
 525 |        "      <td>1</td>\n",
 526 |        "      <td>0</td>\n",
 527 |        "      <td>0</td>\n",
 528 |        "      <td>1</td>\n",
 529 |        "    </tr>\n",
 530 |        "    <tr>\n",
 531 |        "      <th>4</th>\n",
 532 |        "      <td>4.60</td>\n",
 533 |        "      <td>6.87</td>\n",
 534 |        "      <td>42450</td>\n",
 535 |        "      <td>0</td>\n",
 536 |        "      <td>6</td>\n",
 537 |        "      <td>0</td>\n",
 538 |        "      <td>1</td>\n",
 539 |        "      <td>0</td>\n",
 540 |        "      <td>1</td>\n",
 541 |        "      <td>0</td>\n",
 542 |        "      <td>0</td>\n",
 543 |        "      <td>1</td>\n",
 544 |        "    </tr>\n",
 545 |        "  </tbody>\n",
 546 |        "</table>\n",
 547 |        "</div>"
 548 |       ],
 549 |       "text/plain": [
 550 |        "   Selling_Price  Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_CNG  \\\n",
 551 |        "0           3.35           5.59       27000      0        6              0   \n",
 552 |        "1           4.75           9.54       43000      0        7              0   \n",
 553 |        "2           7.25           9.85        6900      0        3              0   \n",
 554 |        "3           2.85           4.15        5200      0        9              0   \n",
 555 |        "4           4.60           6.87       42450      0        6              0   \n",
 556 |        "\n",
 557 |        "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Dealer  \\\n",
 558 |        "0                 0                 1                   1   \n",
 559 |        "1                 1                 0                   1   \n",
 560 |        "2                 0                 1                   1   \n",
 561 |        "3                 0                 1                   1   \n",
 562 |        "4                 1                 0                   1   \n",
 563 |        "\n",
 564 |        "   Seller_Type_Individual  Transmission_Automatic  Transmission_Manual  \n",
 565 |        "0                       0                       0                    1  \n",
 566 |        "1                       0                       0                    1  \n",
 567 |        "2                       0                       0                    1  \n",
 568 |        "3                       0                       0                    1  \n",
 569 |        "4                       0                       0                    1  "
 570 |       ]
 571 |      },
 572 |      "execution_count": 9,
 573 |      "metadata": {},
 574 |      "output_type": "execute_result"
 575 |     }
 576 |    ],
 577 |    "source": [
 578 |     "df.head()"
 579 |    ]
 580 |   },
 581 |   {
 582 |    "cell_type": "code",
 583 |    "execution_count": 10,
 584 |    "metadata": {},
 585 |    "outputs": [],
 586 |    "source": [
 587 |     "x=df.drop('Selling_Price',axis=1)\n",
 588 |     "y=df['Selling_Price']"
 589 |    ]
 590 |   },
 591 |   {
 592 |    "cell_type": "code",
 593 |    "execution_count": 11,
 594 |    "metadata": {},
 595 |    "outputs": [
 596 |     {
 597 |      "data": {
 598 |       "text/html": [
 599 |        "<div>\n",
 600 |        "<style scoped>\n",
 601 |        "    .dataframe tbody tr th:only-of-type {\n",
 602 |        "        vertical-align: middle;\n",
 603 |        "    }\n",
 604 |        "\n",
 605 |        "    .dataframe tbody tr th {\n",
 606 |        "        vertical-align: top;\n",
 607 |        "    }\n",
 608 |        "\n",
 609 |        "    .dataframe thead th {\n",
 610 |        "        text-align: right;\n",
 611 |        "    }\n",
 612 |        "</style>\n",
 613 |        "<table border=\"1\" class=\"dataframe\">\n",
 614 |        "  <thead>\n",
 615 |        "    <tr style=\"text-align: right;\">\n",
 616 |        "      <th></th>\n",
 617 |        "      <th>Present_Price</th>\n",
 618 |        "      <th>Kms_Driven</th>\n",
 619 |        "      <th>Owner</th>\n",
 620 |        "      <th>no_year</th>\n",
 621 |        "      <th>Fuel_Type_CNG</th>\n",
 622 |        "      <th>Fuel_Type_Diesel</th>\n",
 623 |        "      <th>Fuel_Type_Petrol</th>\n",
 624 |        "      <th>Seller_Type_Dealer</th>\n",
 625 |        "      <th>Seller_Type_Individual</th>\n",
 626 |        "      <th>Transmission_Automatic</th>\n",
 627 |        "      <th>Transmission_Manual</th>\n",
 628 |        "    </tr>\n",
 629 |        "  </thead>\n",
 630 |        "  <tbody>\n",
 631 |        "    <tr>\n",
 632 |        "      <th>0</th>\n",
 633 |        "      <td>5.59</td>\n",
 634 |        "      <td>27000</td>\n",
 635 |        "      <td>0</td>\n",
 636 |        "      <td>6</td>\n",
 637 |        "      <td>0</td>\n",
 638 |        "      <td>0</td>\n",
 639 |        "      <td>1</td>\n",
 640 |        "      <td>1</td>\n",
 641 |        "      <td>0</td>\n",
 642 |        "      <td>0</td>\n",
 643 |        "      <td>1</td>\n",
 644 |        "    </tr>\n",
 645 |        "    <tr>\n",
 646 |        "      <th>1</th>\n",
 647 |        "      <td>9.54</td>\n",
 648 |        "      <td>43000</td>\n",
 649 |        "      <td>0</td>\n",
 650 |        "      <td>7</td>\n",
 651 |        "      <td>0</td>\n",
 652 |        "      <td>1</td>\n",
 653 |        "      <td>0</td>\n",
 654 |        "      <td>1</td>\n",
 655 |        "      <td>0</td>\n",
 656 |        "      <td>0</td>\n",
 657 |        "      <td>1</td>\n",
 658 |        "    </tr>\n",
 659 |        "    <tr>\n",
 660 |        "      <th>2</th>\n",
 661 |        "      <td>9.85</td>\n",
 662 |        "      <td>6900</td>\n",
 663 |        "      <td>0</td>\n",
 664 |        "      <td>3</td>\n",
 665 |        "      <td>0</td>\n",
 666 |        "      <td>0</td>\n",
 667 |        "      <td>1</td>\n",
 668 |        "      <td>1</td>\n",
 669 |        "      <td>0</td>\n",
 670 |        "      <td>0</td>\n",
 671 |        "      <td>1</td>\n",
 672 |        "    </tr>\n",
 673 |        "    <tr>\n",
 674 |        "      <th>3</th>\n",
 675 |        "      <td>4.15</td>\n",
 676 |        "      <td>5200</td>\n",
 677 |        "      <td>0</td>\n",
 678 |        "      <td>9</td>\n",
 679 |        "      <td>0</td>\n",
 680 |        "      <td>0</td>\n",
 681 |        "      <td>1</td>\n",
 682 |        "      <td>1</td>\n",
 683 |        "      <td>0</td>\n",
 684 |        "      <td>0</td>\n",
 685 |        "      <td>1</td>\n",
 686 |        "    </tr>\n",
 687 |        "    <tr>\n",
 688 |        "      <th>4</th>\n",
 689 |        "      <td>6.87</td>\n",
 690 |        "      <td>42450</td>\n",
 691 |        "      <td>0</td>\n",
 692 |        "      <td>6</td>\n",
 693 |        "      <td>0</td>\n",
 694 |        "      <td>1</td>\n",
 695 |        "      <td>0</td>\n",
 696 |        "      <td>1</td>\n",
 697 |        "      <td>0</td>\n",
 698 |        "      <td>0</td>\n",
 699 |        "      <td>1</td>\n",
 700 |        "    </tr>\n",
 701 |        "  </tbody>\n",
 702 |        "</table>\n",
 703 |        "</div>"
 704 |       ],
 705 |       "text/plain": [
 706 |        "   Present_Price  Kms_Driven  Owner  no_year  Fuel_Type_CNG  Fuel_Type_Diesel  \\\n",
 707 |        "0           5.59       27000      0        6              0                 0   \n",
 708 |        "1           9.54       43000      0        7              0                 1   \n",
 709 |        "2           9.85        6900      0        3              0                 0   \n",
 710 |        "3           4.15        5200      0        9              0                 0   \n",
 711 |        "4           6.87       42450      0        6              0                 1   \n",
 712 |        "\n",
 713 |        "   Fuel_Type_Petrol  Seller_Type_Dealer  Seller_Type_Individual  \\\n",
 714 |        "0                 1                   1                       0   \n",
 715 |        "1                 0                   1                       0   \n",
 716 |        "2                 1                   1                       0   \n",
 717 |        "3                 1                   1                       0   \n",
 718 |        "4                 0                   1                       0   \n",
 719 |        "\n",
 720 |        "   Transmission_Automatic  Transmission_Manual  \n",
 721 |        "0                       0                    1  \n",
 722 |        "1                       0                    1  \n",
 723 |        "2                       0                    1  \n",
 724 |        "3                       0                    1  \n",
 725 |        "4                       0                    1  "
 726 |       ]
 727 |      },
 728 |      "execution_count": 11,
 729 |      "metadata": {},
 730 |      "output_type": "execute_result"
 731 |     }
 732 |    ],
 733 |    "source": [
 734 |     "x.head()"
 735 |    ]
 736 |   },
 737 |   {
 738 |    "cell_type": "code",
 739 |    "execution_count": 12,
 740 |    "metadata": {},
 741 |    "outputs": [
 742 |     {
 743 |      "data": {
 744 |       "text/plain": [
 745 |        "0    3.35\n",
 746 |        "1    4.75\n",
 747 |        "2    7.25\n",
 748 |        "3    2.85\n",
 749 |        "4    4.60\n",
 750 |        "Name: Selling_Price, dtype: float64"
 751 |       ]
 752 |      },
 753 |      "execution_count": 12,
 754 |      "metadata": {},
 755 |      "output_type": "execute_result"
 756 |     }
 757 |    ],
 758 |    "source": [
 759 |     "y.head()"
 760 |    ]
 761 |   },
 762 |   {
 763 |    "cell_type": "code",
 764 |    "execution_count": 13,
 765 |    "metadata": {},
 766 |    "outputs": [],
 767 |    "source": [
 768 |     "from sklearn.model_selection import train_test_split"
 769 |    ]
 770 |   },
 771 |   {
 772 |    "cell_type": "code",
 773 |    "execution_count": 14,
 774 |    "metadata": {},
 775 |    "outputs": [],
 776 |    "source": [
 777 |     "x_train, x_test, y_train, y_test = train_test_split(x,y,random_state=5)"
 778 |    ]
 779 |   },
 780 |   {
 781 |    "cell_type": "code",
 782 |    "execution_count": 15,
 783 |    "metadata": {},
 784 |    "outputs": [
 785 |     {
 786 |      "data": {
 787 |       "text/plain": [
 788 |        "((225, 11), (225,))"
 789 |       ]
 790 |      },
 791 |      "execution_count": 15,
 792 |      "metadata": {},
 793 |      "output_type": "execute_result"
 794 |     }
 795 |    ],
 796 |    "source": [
 797 |     "x_train.shape , y_train.shape"
 798 |    ]
 799 |   },
 800 |   {
 801 |    "cell_type": "code",
 802 |    "execution_count": 16,
 803 |    "metadata": {},
 804 |    "outputs": [],
 805 |    "source": [
 806 |     "from sklearn.linear_model import LinearRegression"
 807 |    ]
 808 |   },
 809 |   {
 810 |    "cell_type": "code",
 811 |    "execution_count": 17,
 812 |    "metadata": {},
 813 |    "outputs": [
 814 |     {
 815 |      "data": {
 816 |       "text/plain": [
 817 |        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
 818 |       ]
 819 |      },
 820 |      "execution_count": 17,
 821 |      "metadata": {},
 822 |      "output_type": "execute_result"
 823 |     }
 824 |    ],
 825 |    "source": [
 826 |     "lr=LinearRegression()\n",
 827 |     "lr.fit(x_train,y_train)"
 828 |    ]
 829 |   },
 830 |   {
 831 |    "cell_type": "code",
 832 |    "execution_count": 18,
 833 |    "metadata": {},
 834 |    "outputs": [],
 835 |    "source": [
 836 |     "prediction = lr.predict(x_test)"
 837 |    ]
 838 |   },
 839 |   {
 840 |    "cell_type": "code",
 841 |    "execution_count": 19,
 842 |    "metadata": {},
 843 |    "outputs": [],
 844 |    "source": [
 845 |     "import matplotlib.pyplot as plt\n",
 846 |     "import seaborn as sns"
 847 |    ]
 848 |   },
 849 |   {
 850 |    "cell_type": "code",
 851 |    "execution_count": 20,
 852 |    "metadata": {},
 853 |    "outputs": [
 854 |     {
 855 |      "data": {
 856 |       "text/plain": [
 857 |        "<matplotlib.collections.PathCollection at 0x1b09bba2c48>"
 858 |       ]
 859 |      },
 860 |      "execution_count": 20,
 861 |      "metadata": {},
 862 |      "output_type": "execute_result"
 863 |     },
 864 |     {
 865 |      "data": {
 866 |       "image/png": "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\n",
 867 |       "text/plain": [
 868 |        "<Figure size 432x288 with 1 Axes>"
 869 |       ]
 870 |      },
 871 |      "metadata": {
 872 |       "needs_background": "light"
 873 |      },
 874 |      "output_type": "display_data"
 875 |     }
 876 |    ],
 877 |    "source": [
 878 |     "plt.scatter(y_test,prediction)"
 879 |    ]
 880 |   },
 881 |   {
 882 |    "cell_type": "code",
 883 |    "execution_count": 21,
 884 |    "metadata": {},
 885 |    "outputs": [
 886 |     {
 887 |      "data": {
 888 |       "text/plain": [
 889 |        "<matplotlib.axes._subplots.AxesSubplot at 0x1b09d333908>"
 890 |       ]
 891 |      },
 892 |      "execution_count": 21,
 893 |      "metadata": {},
 894 |      "output_type": "execute_result"
 895 |     },
 896 |     {
 897 |      "data": {
 898 |       "image/png": 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\n",
 899 |       "text/plain": [
 900 |        "<Figure size 432x288 with 1 Axes>"
 901 |       ]
 902 |      },
 903 |      "metadata": {
 904 |       "needs_background": "light"
 905 |      },
 906 |      "output_type": "display_data"
 907 |     }
 908 |    ],
 909 |    "source": [
 910 |     "sns.distplot(y_test-prediction)"
 911 |    ]
 912 |   },
 913 |   {
 914 |    "cell_type": "code",
 915 |    "execution_count": 22,
 916 |    "metadata": {},
 917 |    "outputs": [],
 918 |    "source": [
 919 |     "from sklearn import metrics\n",
 920 |     "import numpy as np"
 921 |    ]
 922 |   },
 923 |   {
 924 |    "cell_type": "code",
 925 |    "execution_count": 23,
 926 |    "metadata": {},
 927 |    "outputs": [
 928 |     {
 929 |      "name": "stdout",
 930 |      "output_type": "stream",
 931 |      "text": [
 932 |       "MAE:  1.140996108785903\n",
 933 |       "MSE:  3.2815963416575036\n",
 934 |       "RMSE:  1.8115176901309862\n",
 935 |       "R squared:  0.8608525898499443\n"
 936 |      ]
 937 |     }
 938 |    ],
 939 |    "source": [
 940 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
 941 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
 942 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
 943 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
 944 |    ]
 945 |   },
 946 |   {
 947 |    "cell_type": "code",
 948 |    "execution_count": 25,
 949 |    "metadata": {},
 950 |    "outputs": [],
 951 |    "source": [
 952 |     "from sklearn.linear_model import Ridge"
 953 |    ]
 954 |   },
 955 |   {
 956 |    "cell_type": "code",
 957 |    "execution_count": 26,
 958 |    "metadata": {},
 959 |    "outputs": [],
 960 |    "source": [
 961 |     "ridge=Ridge()\n",
 962 |     "ridge.fit(x_train,y_train)\n",
 963 |     "prediction = ridge.predict(x_test)"
 964 |    ]
 965 |   },
 966 |   {
 967 |    "cell_type": "code",
 968 |    "execution_count": 27,
 969 |    "metadata": {},
 970 |    "outputs": [
 971 |     {
 972 |      "name": "stdout",
 973 |      "output_type": "stream",
 974 |      "text": [
 975 |       "MAE:  1.1332339163458571\n",
 976 |       "MSE:  3.2738266436526455\n",
 977 |       "RMSE:  1.8093718920256956\n",
 978 |       "R squared:  0.8611820433361332\n"
 979 |      ]
 980 |     }
 981 |    ],
 982 |    "source": [
 983 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
 984 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
 985 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
 986 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
 987 |    ]
 988 |   },
 989 |   {
 990 |    "cell_type": "code",
 991 |    "execution_count": 28,
 992 |    "metadata": {},
 993 |    "outputs": [],
 994 |    "source": [
 995 |     "from sklearn.linear_model import Lasso"
 996 |    ]
 997 |   },
 998 |   {
 999 |    "cell_type": "code",
1000 |    "execution_count": 29,
1001 |    "metadata": {},
1002 |    "outputs": [
1003 |     {
1004 |      "name": "stdout",
1005 |      "output_type": "stream",
1006 |      "text": [
1007 |       "MAE:  1.3349151159008392\n",
1008 |       "MSE:  4.47313681445178\n",
1009 |       "RMSE:  2.1149791522499175\n",
1010 |       "R squared:  0.8103284687770421\n"
1011 |      ]
1012 |     }
1013 |    ],
1014 |    "source": [
1015 |     "ridge=Lasso()\n",
1016 |     "ridge.fit(x_train,y_train)\n",
1017 |     "prediction = ridge.predict(x_test)\n",
1018 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
1019 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
1020 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
1021 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
1022 |    ]
1023 |   },
1024 |   {
1025 |    "cell_type": "code",
1026 |    "execution_count": 30,
1027 |    "metadata": {},
1028 |    "outputs": [],
1029 |    "source": [
1030 |     "from sklearn.model_selection import RandomizedSearchCV,GridSearchCV"
1031 |    ]
1032 |   },
1033 |   {
1034 |    "cell_type": "code",
1035 |    "execution_count": 31,
1036 |    "metadata": {},
1037 |    "outputs": [
1038 |     {
1039 |      "name": "stdout",
1040 |      "output_type": "stream",
1041 |      "text": [
1042 |       "MAE:  1.1187640189415795\n",
1043 |       "MSE:  3.2349381933942647\n",
1044 |       "RMSE:  1.798593393014181\n",
1045 |       "R squared:  0.8628310051750747\n"
1046 |      ]
1047 |     },
1048 |     {
1049 |      "name": "stderr",
1050 |      "output_type": "stream",
1051 |      "text": [
1052 |       "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:281: UserWarning: The total space of parameters 2 is smaller than n_iter=10. Running 2 iterations. For exhaustive searches, use GridSearchCV.\n",
1053 |       "  % (grid_size, self.n_iter, grid_size), UserWarning)\n"
1054 |      ]
1055 |     }
1056 |    ],
1057 |    "source": [
1058 |     "r=Ridge()\n",
1059 |     "parameters={'alpha':[1,20]}\n",
1060 |     "l=RandomizedSearchCV(r,parameters,scoring=\"neg_mean_squared_error\",cv=5)\n",
1061 |     "l.fit(x_train,y_train)\n",
1062 |     "prediction = l.predict(x_test)\n",
1063 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
1064 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
1065 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
1066 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
1067 |    ]
1068 |   },
1069 |   {
1070 |    "cell_type": "code",
1071 |    "execution_count": 32,
1072 |    "metadata": {},
1073 |    "outputs": [
1074 |     {
1075 |      "name": "stdout",
1076 |      "output_type": "stream",
1077 |      "text": [
1078 |       "MAE:  1.1194605545945522\n",
1079 |       "MSE:  3.235117272776871\n",
1080 |       "RMSE:  1.798643175501153\n",
1081 |       "R squared:  0.8628234117876782\n"
1082 |      ]
1083 |     }
1084 |    ],
1085 |    "source": [
1086 |     "r=Ridge()\n",
1087 |     "parameters={'alpha':[1000,100,30,40,50]}\n",
1088 |     "l=GridSearchCV(r,parameters,scoring=\"neg_mean_squared_error\",cv=5)\n",
1089 |     "l.fit(x_train,y_train)\n",
1090 |     "prediction = l.predict(x_test)\n",
1091 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
1092 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
1093 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
1094 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
1095 |    ]
1096 |   },
1097 |   {
1098 |    "cell_type": "code",
1099 |    "execution_count": 33,
1100 |    "metadata": {},
1101 |    "outputs": [
1102 |     {
1103 |      "data": {
1104 |       "text/plain": [
1105 |        "{'alpha': 30}"
1106 |       ]
1107 |      },
1108 |      "execution_count": 33,
1109 |      "metadata": {},
1110 |      "output_type": "execute_result"
1111 |     }
1112 |    ],
1113 |    "source": [
1114 |     "l.best_params_"
1115 |    ]
1116 |   },
1117 |   {
1118 |    "cell_type": "code",
1119 |    "execution_count": 24,
1120 |    "metadata": {},
1121 |    "outputs": [],
1122 |    "source": [
1123 |     "from sklearn.ensemble import RandomForestRegressor"
1124 |    ]
1125 |   },
1126 |   {
1127 |    "cell_type": "code",
1128 |    "execution_count": 35,
1129 |    "metadata": {},
1130 |    "outputs": [],
1131 |    "source": [
1132 |     "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
1133 |     "# Number of features to consider at every split\n",
1134 |     "max_features = ['auto', 'sqrt']\n",
1135 |     "# Maximum number of levels in tree\n",
1136 |     "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
1137 |     "max_depth.append(None)\n",
1138 |     "# Minimum number of samples required to split a node\n",
1139 |     "min_samples_split = [2, 5, 10]\n",
1140 |     "# Minimum number of samples required at each leaf node\n",
1141 |     "min_samples_leaf = [1, 2, 4]\n",
1142 |     "# Method of selecting samples for training each tree\n",
1143 |     "bootstrap = [True, False]\n",
1144 |     "# Create the random grid\n",
1145 |     "random_grid = {'n_estimators': n_estimators,\n",
1146 |     "               'max_features': max_features,\n",
1147 |     "               'max_depth': max_depth,\n",
1148 |     "               'min_samples_split': min_samples_split,\n",
1149 |     "               'min_samples_leaf': min_samples_leaf,\n",
1150 |     "               'bootstrap': bootstrap}"
1151 |    ]
1152 |   },
1153 |   {
1154 |    "cell_type": "code",
1155 |    "execution_count": 49,
1156 |    "metadata": {},
1157 |    "outputs": [
1158 |     {
1159 |      "name": "stdout",
1160 |      "output_type": "stream",
1161 |      "text": [
1162 |       "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
1163 |      ]
1164 |     },
1165 |     {
1166 |      "name": "stderr",
1167 |      "output_type": "stream",
1168 |      "text": [
1169 |       "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
1170 |       "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   35.2s\n",
1171 |       "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  2.7min\n",
1172 |       "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  5.0min finished\n"
1173 |      ]
1174 |     },
1175 |     {
1176 |      "name": "stdout",
1177 |      "output_type": "stream",
1178 |      "text": [
1179 |       "MAE:  0.565409210526331\n",
1180 |       "MSE:  1.071250076830035\n",
1181 |       "RMSE:  1.0350121143397477\n",
1182 |       "R squared:  0.9545764748937227\n"
1183 |      ]
1184 |     }
1185 |    ],
1186 |    "source": [
1187 |     "r=RandomForestRegressor(n_estimators=100)\n",
1188 |     "rf = RandomForestRegressor()\n",
1189 |     "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
1190 |     "rf_random.fit(x_train,y_train)\n",
1191 |     "prediction = rf_random.predict(x_test)\n",
1192 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
1193 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
1194 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
1195 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
1196 |    ]
1197 |   },
1198 |   {
1199 |    "cell_type": "code",
1200 |    "execution_count": 36,
1201 |    "metadata": {},
1202 |    "outputs": [
1203 |     {
1204 |      "name": "stdout",
1205 |      "output_type": "stream",
1206 |      "text": [
1207 |       "MAE:  0.49281447368421116\n",
1208 |       "MSE:  0.603024184078948\n",
1209 |       "RMSE:  0.7765463180512467\n",
1210 |       "R squared:  0.9744303550051941\n"
1211 |      ]
1212 |     }
1213 |    ],
1214 |    "source": [
1215 |     "r=RandomForestRegressor(n_estimators=100)\n",
1216 |     "\n",
1217 |     "r.fit(x_train,y_train)\n",
1218 |     "prediction = r.predict(x_test)\n",
1219 |     "print('MAE: ',metrics.mean_absolute_error(y_test,prediction))\n",
1220 |     "print('MSE: ',metrics.mean_squared_error(y_test,prediction))\n",
1221 |     "print('RMSE: ',np.sqrt(metrics.mean_squared_error(y_test,prediction)))\n",
1222 |     "print('R squared: ',metrics.r2_score(y_test,prediction))"
1223 |    ]
1224 |   },
1225 |   {
1226 |    "cell_type": "code",
1227 |    "execution_count": 38,
1228 |    "metadata": {},
1229 |    "outputs": [],
1230 |    "source": [
1231 |     "import pickle \n",
1232 |     "pickle.dump(r,open('model.pkl','wb'))"
1233 |    ]
1234 |   },
1235 |   {
1236 |    "cell_type": "code",
1237 |    "execution_count": null,
1238 |    "metadata": {},
1239 |    "outputs": [],
1240 |    "source": []
1241 |   }
1242 |  ],
1243 |  "metadata": {
1244 |   "kernelspec": {
1245 |    "display_name": "Python 3",
1246 |    "language": "python",
1247 |    "name": "python3"
1248 |   },
1249 |   "language_info": {
1250 |    "codemirror_mode": {
1251 |     "name": "ipython",
1252 |     "version": 3
1253 |    },
1254 |    "file_extension": ".py",
1255 |    "mimetype": "text/x-python",
1256 |    "name": "python",
1257 |    "nbconvert_exporter": "python",
1258 |    "pygments_lexer": "ipython3",
1259 |    "version": "3.7.6"
1260 |   }
1261 |  },
1262 |  "nbformat": 4,
1263 |  "nbformat_minor": 4
1264 | }
1265 | 


--------------------------------------------------------------------------------
/Procfile:
--------------------------------------------------------------------------------
1 | web: gunicorn app:app
2 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Car-Price-Prediction
 2 | * This is a machine learning model made using python and flask which predicts the selling price of second-hand cars.
 3 | * Do ⭐ the repository, if it helped you in anyway.
 4 | * If you want to try this out, head to https://car-price-pr.herokuapp.com
 5 | 
 6 | ## Data source:
 7 | https://www.kaggle.com/nehalbirla/vehicle-dataset-from-cardekho?select=car+data.csv
 8 | 
 9 | ## Dependencies:
10 | * Python - 3.7
11 | * Scikit-Learn
12 | * Pandas
13 | * Numpy
14 | * Matplotlib
15 | * Seaborn
16 | 
17 | ## Algorithms Used:
18 | * Linear Regression
19 | * Ridge Regression
20 | * Lasso Regression
21 | * Random Forest Regression
22 | 
23 | 
24 | ## A glimpse of the web app:
25 | ![ezgif com-video-to-gif (1)](https://user-images.githubusercontent.com/49580063/111056632-85d99980-84a6-11eb-8134-d431672dc425.gif)
26 | 


--------------------------------------------------------------------------------
/app.py:
--------------------------------------------------------------------------------
 1 | from flask import Flask, redirect, url_for, request, render_template
 2 | import pickle
 3 | import sklearn
 4 | from sklearn.preprocessing import StandardScaler
 5 | app = Flask(__name__)
 6 | model = pickle.load(open('model.pkl', 'rb'))
 7 | standard_to = StandardScaler()
 8 | 
 9 | 
10 | @app.route('/',methods=['GET'])
11 | def Home():
12 |     return render_template('index.html')
13 | 
14 | 
15 | @app.route("/predict", methods=['POST'])
16 | def predict():
17 |     Fuel_Type_Diesel=0
18 |     if request.method == 'POST':
19 |         Year = int(request.form['Year'])
20 |         Present_Price=float(request.form['Present_Price'])
21 |         Kms_Driven=int(request.form['Kms_Driven'])
22 |         Owner=int(request.form['Owner'])
23 |         Fuel_Type_Petrol=request.form['Fuel_Type_Petrol']
24 |         if(Fuel_Type_Petrol=='CNG'):
25 |                 Fuel_Type_CNG=1
26 |                 Fuel_Type_Diesel=0
27 |                 Fuel_Type_Petrol=0
28 |         elif(Fuel_Type_Petrol=='Diesel'):
29 |                 Fuel_Type_CNG=0
30 |                 Fuel_Type_Diesel=1
31 |                 Fuel_Type_Petrol=0
32 |         else:
33 |                 Fuel_Type_CNG=0
34 |                 Fuel_Type_Diesel=0
35 |                 Fuel_Type_Petrol=1
36 | 
37 |         Year=2020-Year
38 |         Seller_Type_Individual=request.form['Seller_Type_Individual']
39 |         if(Seller_Type_Individual=='Individual'):
40 |             Seller_Type_Individual=1
41 |             Seller_Type_Dealer=0
42 |         else:
43 |             Seller_Type_Dealer=1
44 |             Seller_Type_Individual=0	
45 |         Transmission_Mannual=request.form['Transmission_Mannual']
46 |         if(Transmission_Mannual=='Mannual'):
47 |             Transmission_Mannual=1
48 |             Transmission_Automatic=0
49 |         else:
50 |             Transmission_Automatic=1
51 |             Transmission_Mannual=0
52 |         prediction=model.predict([[Present_Price,Kms_Driven,Owner,Year,Fuel_Type_CNG,Fuel_Type_Diesel,Fuel_Type_Petrol,Seller_Type_Dealer,Seller_Type_Individual,Transmission_Automatic,Transmission_Mannual]])
53 |         output=round(prediction[0],2)
54 |         if output<0:
55 |             return render_template('index.html',prediction_texts="Sorry you cannot sell this car")
56 |         else:
57 |             return render_template('index.html',prediction_text="You Can Sell Your Car at {} Lakhs".format(output))
58 |     else:
59 |         return render_template('index.html')
60 | 
61 | if __name__=="__main__":
62 |     app.run(debug = True)
63 | 
64 | 


--------------------------------------------------------------------------------
/car data.csv:
--------------------------------------------------------------------------------
  1 | Car_Name,Year,Selling_Price,Present_Price,Kms_Driven,Fuel_Type,Seller_Type,Transmission,Owner
  2 | ritz,2014,3.35,5.59,27000,Petrol,Dealer,Manual,0
  3 | sx4,2013,4.75,9.54,43000,Diesel,Dealer,Manual,0
  4 | ciaz,2017,7.25,9.85,6900,Petrol,Dealer,Manual,0
  5 | wagon r,2011,2.85,4.15,5200,Petrol,Dealer,Manual,0
  6 | swift,2014,4.6,6.87,42450,Diesel,Dealer,Manual,0
  7 | vitara brezza,2018,9.25,9.83,2071,Diesel,Dealer,Manual,0
  8 | ciaz,2015,6.75,8.12,18796,Petrol,Dealer,Manual,0
  9 | s cross,2015,6.5,8.61,33429,Diesel,Dealer,Manual,0
 10 | ciaz,2016,8.75,8.89,20273,Diesel,Dealer,Manual,0
 11 | ciaz,2015,7.45,8.92,42367,Diesel,Dealer,Manual,0
 12 | alto 800,2017,2.85,3.6,2135,Petrol,Dealer,Manual,0
 13 | ciaz,2015,6.85,10.38,51000,Diesel,Dealer,Manual,0
 14 | ciaz,2015,7.5,9.94,15000,Petrol,Dealer,Automatic,0
 15 | ertiga,2015,6.1,7.71,26000,Petrol,Dealer,Manual,0
 16 | dzire,2009,2.25,7.21,77427,Petrol,Dealer,Manual,0
 17 | ertiga,2016,7.75,10.79,43000,Diesel,Dealer,Manual,0
 18 | ertiga,2015,7.25,10.79,41678,Diesel,Dealer,Manual,0
 19 | ertiga,2016,7.75,10.79,43000,Diesel,Dealer,Manual,0
 20 | wagon r,2015,3.25,5.09,35500,CNG,Dealer,Manual,0
 21 | sx4,2010,2.65,7.98,41442,Petrol,Dealer,Manual,0
 22 | alto k10,2016,2.85,3.95,25000,Petrol,Dealer,Manual,0
 23 | ignis,2017,4.9,5.71,2400,Petrol,Dealer,Manual,0
 24 | sx4,2011,4.4,8.01,50000,Petrol,Dealer,Automatic,0
 25 | alto k10,2014,2.5,3.46,45280,Petrol,Dealer,Manual,0
 26 | wagon r,2013,2.9,4.41,56879,Petrol,Dealer,Manual,0
 27 | swift,2011,3,4.99,20000,Petrol,Dealer,Manual,0
 28 | swift,2013,4.15,5.87,55138,Petrol,Dealer,Manual,0
 29 | swift,2017,6,6.49,16200,Petrol,Individual,Manual,0
 30 | alto k10,2010,1.95,3.95,44542,Petrol,Dealer,Manual,0
 31 | ciaz,2015,7.45,10.38,45000,Diesel,Dealer,Manual,0
 32 | ritz,2012,3.1,5.98,51439,Diesel,Dealer,Manual,0
 33 | ritz,2011,2.35,4.89,54200,Petrol,Dealer,Manual,0
 34 | swift,2014,4.95,7.49,39000,Diesel,Dealer,Manual,0
 35 | ertiga,2014,6,9.95,45000,Diesel,Dealer,Manual,0
 36 | dzire,2014,5.5,8.06,45000,Diesel,Dealer,Manual,0
 37 | sx4,2011,2.95,7.74,49998,CNG,Dealer,Manual,0
 38 | dzire,2015,4.65,7.2,48767,Petrol,Dealer,Manual,0
 39 | 800,2003,0.35,2.28,127000,Petrol,Individual,Manual,0
 40 | alto k10,2016,3,3.76,10079,Petrol,Dealer,Manual,0
 41 | sx4,2003,2.25,7.98,62000,Petrol,Dealer,Manual,0
 42 | baleno,2016,5.85,7.87,24524,Petrol,Dealer,Automatic,0
 43 | alto k10,2014,2.55,3.98,46706,Petrol,Dealer,Manual,0
 44 | sx4,2008,1.95,7.15,58000,Petrol,Dealer,Manual,0
 45 | dzire,2014,5.5,8.06,45780,Diesel,Dealer,Manual,0
 46 | omni,2012,1.25,2.69,50000,Petrol,Dealer,Manual,0
 47 | ciaz,2014,7.5,12.04,15000,Petrol,Dealer,Automatic,0
 48 | ritz,2013,2.65,4.89,64532,Petrol,Dealer,Manual,0
 49 | wagon r,2006,1.05,4.15,65000,Petrol,Dealer,Manual,0
 50 | ertiga,2015,5.8,7.71,25870,Petrol,Dealer,Manual,0
 51 | ciaz,2017,7.75,9.29,37000,Petrol,Dealer,Automatic,0
 52 | fortuner,2012,14.9,30.61,104707,Diesel,Dealer,Automatic,0
 53 | fortuner,2015,23,30.61,40000,Diesel,Dealer,Automatic,0
 54 | innova,2017,18,19.77,15000,Diesel,Dealer,Automatic,0
 55 | fortuner,2013,16,30.61,135000,Diesel,Individual,Automatic,0
 56 | innova,2005,2.75,10.21,90000,Petrol,Individual,Manual,0
 57 | corolla altis,2009,3.6,15.04,70000,Petrol,Dealer,Automatic,0
 58 | etios cross,2015,4.5,7.27,40534,Petrol,Dealer,Manual,0
 59 | corolla altis,2010,4.75,18.54,50000,Petrol,Dealer,Manual,0
 60 | etios g,2014,4.1,6.8,39485,Petrol,Dealer,Manual,1
 61 | fortuner,2014,19.99,35.96,41000,Diesel,Dealer,Automatic,0
 62 | corolla altis,2013,6.95,18.61,40001,Petrol,Dealer,Manual,0
 63 | etios cross,2015,4.5,7.7,40588,Petrol,Dealer,Manual,0
 64 | fortuner,2014,18.75,35.96,78000,Diesel,Dealer,Automatic,0
 65 | fortuner,2015,23.5,35.96,47000,Diesel,Dealer,Automatic,0
 66 | fortuner,2017,33,36.23,6000,Diesel,Dealer,Automatic,0
 67 | etios liva,2014,4.75,6.95,45000,Diesel,Dealer,Manual,0
 68 | innova,2017,19.75,23.15,11000,Petrol,Dealer,Automatic,0
 69 | fortuner,2010,9.25,20.45,59000,Diesel,Dealer,Manual,0
 70 | corolla altis,2011,4.35,13.74,88000,Petrol,Dealer,Manual,0
 71 | corolla altis,2016,14.25,20.91,12000,Petrol,Dealer,Manual,0
 72 | etios liva,2014,3.95,6.76,71000,Diesel,Dealer,Manual,0
 73 | corolla altis,2011,4.5,12.48,45000,Diesel,Dealer,Manual,0
 74 | corolla altis,2013,7.45,18.61,56001,Petrol,Dealer,Manual,0
 75 | etios liva,2011,2.65,5.71,43000,Petrol,Dealer,Manual,0
 76 | etios cross,2014,4.9,8.93,83000,Diesel,Dealer,Manual,0
 77 | etios g,2015,3.95,6.8,36000,Petrol,Dealer,Manual,0
 78 | corolla altis,2013,5.5,14.68,72000,Petrol,Dealer,Manual,0
 79 | corolla,2004,1.5,12.35,135154,Petrol,Dealer,Automatic,0
 80 | corolla altis,2010,5.25,22.83,80000,Petrol,Dealer,Automatic,0
 81 | fortuner,2012,14.5,30.61,89000,Diesel,Dealer,Automatic,0
 82 | corolla altis,2016,14.73,14.89,23000,Diesel,Dealer,Manual,0
 83 | etios gd,2015,4.75,7.85,40000,Diesel,Dealer,Manual,0
 84 | innova,2017,23,25.39,15000,Diesel,Dealer,Automatic,0
 85 | innova,2015,12.5,13.46,38000,Diesel,Dealer,Manual,0
 86 | innova,2005,3.49,13.46,197176,Diesel,Dealer,Manual,0
 87 | camry,2006,2.5,23.73,142000,Petrol,Individual,Automatic,3
 88 | land cruiser,2010,35,92.6,78000,Diesel,Dealer,Manual,0
 89 | corolla altis,2012,5.9,13.74,56000,Petrol,Dealer,Manual,0
 90 | etios liva,2013,3.45,6.05,47000,Petrol,Dealer,Manual,0
 91 | etios g,2014,4.75,6.76,40000,Petrol,Dealer,Manual,0
 92 | corolla altis,2009,3.8,18.61,62000,Petrol,Dealer,Manual,0
 93 | innova,2014,11.25,16.09,58242,Diesel,Dealer,Manual,0
 94 | innova,2005,3.51,13.7,75000,Petrol,Dealer,Manual,0
 95 | fortuner,2015,23,30.61,40000,Diesel,Dealer,Automatic,0
 96 | corolla altis,2008,4,22.78,89000,Petrol,Dealer,Automatic,0
 97 | corolla altis,2012,5.85,18.61,72000,Petrol,Dealer,Manual,0
 98 | innova,2016,20.75,25.39,29000,Diesel,Dealer,Automatic,0
 99 | corolla altis,2017,17,18.64,8700,Petrol,Dealer,Manual,0
100 | corolla altis,2013,7.05,18.61,45000,Petrol,Dealer,Manual,0
101 | fortuner,2010,9.65,20.45,50024,Diesel,Dealer,Manual,0
102 | Royal Enfield Thunder 500,2016,1.75,1.9,3000,Petrol,Individual,Manual,0
103 | UM Renegade Mojave,2017,1.7,1.82,1400,Petrol,Individual,Manual,0
104 | KTM RC200,2017,1.65,1.78,4000,Petrol,Individual,Manual,0
105 | Bajaj Dominar 400,2017,1.45,1.6,1200,Petrol,Individual,Manual,0
106 | Royal Enfield Classic 350,2017,1.35,1.47,4100,Petrol,Individual,Manual,0
107 | KTM RC390,2015,1.35,2.37,21700,Petrol,Individual,Manual,0
108 | Hyosung GT250R,2014,1.35,3.45,16500,Petrol,Individual,Manual,1
109 | Royal Enfield Thunder 350,2013,1.25,1.5,15000,Petrol,Individual,Manual,0
110 | Royal Enfield Thunder 350,2016,1.2,1.5,18000,Petrol,Individual,Manual,0
111 | Royal Enfield Classic 350,2017,1.2,1.47,11000,Petrol,Individual,Manual,0
112 | KTM RC200,2016,1.2,1.78,6000,Petrol,Individual,Manual,0
113 | Royal Enfield Thunder 350,2016,1.15,1.5,8700,Petrol,Individual,Manual,0
114 | KTM 390 Duke ,2014,1.15,2.4,7000,Petrol,Individual,Manual,0
115 | Mahindra Mojo XT300,2016,1.15,1.4,35000,Petrol,Individual,Manual,0
116 | Royal Enfield Classic 350,2015,1.15,1.47,17000,Petrol,Individual,Manual,0
117 | Royal Enfield Classic 350,2015,1.11,1.47,17500,Petrol,Individual,Manual,0
118 | Royal Enfield Classic 350,2013,1.1,1.47,33000,Petrol,Individual,Manual,0
119 | Royal Enfield Thunder 500,2015,1.1,1.9,14000,Petrol,Individual,Manual,0
120 | Royal Enfield Classic 350,2015,1.1,1.47,26000,Petrol,Individual,Manual,0
121 | Royal Enfield Thunder 500,2013,1.05,1.9,5400,Petrol,Individual,Manual,0
122 | Bajaj Pulsar RS200,2016,1.05,1.26,5700,Petrol,Individual,Manual,0
123 | Royal Enfield Thunder 350,2011,1.05,1.5,6900,Petrol,Individual,Manual,0
124 | Royal Enfield Bullet 350,2016,1.05,1.17,6000,Petrol,Individual,Manual,0
125 | Royal Enfield Classic 350,2013,1,1.47,46500,Petrol,Individual,Manual,0
126 | Royal Enfield Classic 500,2012,0.95,1.75,11500,Petrol,Individual,Manual,0
127 | Royal Enfield Classic 500,2009,0.9,1.75,40000,Petrol,Individual,Manual,0
128 | Bajaj Avenger 220,2017,0.9,0.95,1300,Petrol,Individual,Manual,0
129 | Bajaj Avenger 150,2016,0.75,0.8,7000,Petrol,Individual,Manual,0
130 | Honda CB Hornet 160R,2017,0.8,0.87,3000,Petrol,Individual,Manual,0
131 | Yamaha FZ S V 2.0,2017,0.78,0.84,5000,Petrol,Individual,Manual,0
132 | Honda CB Hornet 160R,2017,0.75,0.87,11000,Petrol,Individual,Manual,0
133 | Yamaha FZ 16,2015,0.75,0.82,18000,Petrol,Individual,Manual,0
134 | Bajaj Avenger 220,2017,0.75,0.95,3500,Petrol,Individual,Manual,0
135 | Bajaj Avenger 220,2016,0.72,0.95,500,Petrol,Individual,Manual,0
136 | TVS Apache RTR 160,2017,0.65,0.81,11800,Petrol,Individual,Manual,0
137 | Bajaj Pulsar 150,2015,0.65,0.74,5000,Petrol,Individual,Manual,0
138 | Honda CBR 150,2014,0.65,1.2,23500,Petrol,Individual,Manual,0
139 | Hero Extreme,2013,0.65,0.787,16000,Petrol,Individual,Manual,0
140 | Honda CB Hornet 160R,2016,0.6,0.87,15000,Petrol,Individual,Manual,0
141 | Bajaj Avenger 220 dtsi,2015,0.6,0.95,16600,Petrol,Individual,Manual,0
142 | Honda CBR 150,2013,0.6,1.2,32000,Petrol,Individual,Manual,0
143 | Bajaj Avenger 150 street,2016,0.6,0.8,20000,Petrol,Individual,Manual,0
144 | Yamaha FZ  v 2.0,2015,0.6,0.84,29000,Petrol,Individual,Manual,0
145 | Yamaha FZ  v 2.0,2016,0.6,0.84,25000,Petrol,Individual,Manual,0
146 | Bajaj Pulsar  NS 200,2014,0.6,0.99,25000,Petrol,Individual,Manual,0
147 | TVS Apache RTR 160,2012,0.6,0.81,19000,Petrol,Individual,Manual,0
148 | Hero Extreme,2014,0.55,0.787,15000,Petrol,Individual,Manual,0
149 | Yamaha FZ S V 2.0,2015,0.55,0.84,58000,Petrol,Individual,Manual,0
150 | Bajaj Pulsar 220 F,2010,0.52,0.94,45000,Petrol,Individual,Manual,0
151 | Bajaj Pulsar 220 F,2016,0.51,0.94,24000,Petrol,Individual,Manual,0
152 | TVS Apache RTR 180,2011,0.5,0.826,6000,Petrol,Individual,Manual,0
153 | Hero Passion X pro,2016,0.5,0.55,31000,Petrol,Individual,Manual,0
154 | Bajaj Pulsar NS 200,2012,0.5,0.99,13000,Petrol,Individual,Manual,0
155 | Bajaj Pulsar NS 200,2013,0.5,0.99,45000,Petrol,Individual,Manual,0
156 | Yamaha Fazer ,2014,0.5,0.88,8000,Petrol,Individual,Manual,0
157 | Honda Activa 4G,2017,0.48,0.51,4300,Petrol,Individual,Automatic,0
158 | TVS Sport ,2017,0.48,0.52,15000,Petrol,Individual,Manual,0
159 | Yamaha FZ S V 2.0,2015,0.48,0.84,23000,Petrol,Individual,Manual,0
160 | Honda Dream Yuga ,2017,0.48,0.54,8600,Petrol,Individual,Manual,0
161 | Honda Activa 4G,2017,0.45,0.51,4000,Petrol,Individual,Automatic,0
162 | Bajaj Avenger Street 220,2011,0.45,0.95,24000,Petrol,Individual,Manual,0
163 | TVS Apache RTR 180,2014,0.45,0.826,23000,Petrol,Individual,Manual,0
164 | Bajaj Pulsar NS 200,2012,0.45,0.99,14500,Petrol,Individual,Manual,0
165 | Bajaj Avenger 220 dtsi,2010,0.45,0.95,27000,Petrol,Individual,Manual,0
166 | Hero Splender iSmart,2016,0.45,0.54,14000,Petrol,Individual,Manual,0
167 | Activa 3g,2016,0.45,0.54,500,Petrol,Individual,Automatic,0
168 | Hero Passion Pro,2016,0.45,0.55,1000,Petrol,Individual,Manual,0
169 | TVS Apache RTR 160,2014,0.42,0.81,42000,Petrol,Individual,Manual,0
170 | Honda CB Trigger,2013,0.42,0.73,12000,Petrol,Individual,Manual,0
171 | Hero Splender iSmart,2015,0.4,0.54,14000,Petrol,Individual,Manual,0
172 | Yamaha FZ S ,2012,0.4,0.83,5500,Petrol,Individual,Manual,0
173 | Hero Passion Pro,2015,0.4,0.55,6700,Petrol,Individual,Manual,0
174 | Bajaj Pulsar 135 LS,2014,0.4,0.64,13700,Petrol,Individual,Manual,0
175 | Activa 4g,2017,0.4,0.51,1300,Petrol,Individual,Automatic,0
176 | Honda CB Unicorn,2015,0.38,0.72,38600,Petrol,Individual,Manual,0
177 | Hero Honda CBZ extreme,2011,0.38,0.787,75000,Petrol,Individual,Manual,0
178 | Honda Karizma,2011,0.35,1.05,30000,Petrol,Individual,Manual,0
179 | Honda Activa 125,2016,0.35,0.57,24000,Petrol,Individual,Automatic,0
180 | TVS Jupyter,2014,0.35,0.52,19000,Petrol,Individual,Automatic,0
181 | Honda Karizma,2010,0.31,1.05,213000,Petrol,Individual,Manual,0
182 | Hero Honda Passion Pro,2012,0.3,0.51,60000,Petrol,Individual,Manual,0
183 | Hero Splender Plus,2016,0.3,0.48,50000,Petrol,Individual,Manual,0
184 | Honda CB Shine,2013,0.3,0.58,30000,Petrol,Individual,Manual,0
185 | Bajaj Discover 100,2013,0.27,0.47,21000,Petrol,Individual,Manual,0
186 | Bajaj Pulsar 150,2008,0.25,0.75,26000,Petrol,Individual,Manual,1
187 | Suzuki Access 125,2008,0.25,0.58,1900,Petrol,Individual,Automatic,0
188 | TVS Wego,2010,0.25,0.52,22000,Petrol,Individual,Automatic,0
189 | Honda CB twister,2013,0.25,0.51,32000,Petrol,Individual,Manual,0
190 | Hero Glamour,2013,0.25,0.57,18000,Petrol,Individual,Manual,0
191 | Hero Super Splendor,2005,0.2,0.57,55000,Petrol,Individual,Manual,0
192 | Bajaj Pulsar 150,2008,0.2,0.75,60000,Petrol,Individual,Manual,0
193 | Bajaj Discover 125,2012,0.2,0.57,25000,Petrol,Individual,Manual,1
194 | Hero Hunk,2007,0.2,0.75,49000,Petrol,Individual,Manual,1
195 | Hero  Ignitor Disc,2013,0.2,0.65,24000,Petrol,Individual,Manual,1
196 | Hero  CBZ Xtreme,2008,0.2,0.787,50000,Petrol,Individual,Manual,0
197 | Bajaj  ct 100,2015,0.18,0.32,35000,Petrol,Individual,Manual,0
198 | Activa 3g,2008,0.17,0.52,500000,Petrol,Individual,Automatic,0
199 | Honda CB twister,2010,0.16,0.51,33000,Petrol,Individual,Manual,0
200 | Bajaj Discover 125,2011,0.15,0.57,35000,Petrol,Individual,Manual,1
201 | Honda CB Shine,2007,0.12,0.58,53000,Petrol,Individual,Manual,0
202 | Bajaj Pulsar 150,2006,0.1,0.75,92233,Petrol,Individual,Manual,0
203 | i20,2010,3.25,6.79,58000,Diesel,Dealer,Manual,1
204 | grand i10,2015,4.4,5.7,28200,Petrol,Dealer,Manual,0
205 | i10,2011,2.95,4.6,53460,Petrol,Dealer,Manual,0
206 | eon,2015,2.75,4.43,28282,Petrol,Dealer,Manual,0
207 | grand i10,2016,5.25,5.7,3493,Petrol,Dealer,Manual,1
208 | xcent,2017,5.75,7.13,12479,Petrol,Dealer,Manual,0
209 | grand i10,2015,5.15,5.7,34797,Petrol,Dealer,Automatic,0
210 | i20,2017,7.9,8.1,3435,Petrol,Dealer,Manual,0
211 | grand i10,2015,4.85,5.7,21125,Diesel,Dealer,Manual,0
212 | i10,2012,3.1,4.6,35775,Petrol,Dealer,Manual,0
213 | elantra,2015,11.75,14.79,43535,Diesel,Dealer,Manual,0
214 | creta,2016,11.25,13.6,22671,Petrol,Dealer,Manual,0
215 | i20,2011,2.9,6.79,31604,Petrol,Dealer,Manual,0
216 | grand i10,2017,5.25,5.7,20114,Petrol,Dealer,Manual,0
217 | verna,2012,4.5,9.4,36100,Petrol,Dealer,Manual,0
218 | eon,2016,2.9,4.43,12500,Petrol,Dealer,Manual,0
219 | eon,2016,3.15,4.43,15000,Petrol,Dealer,Manual,0
220 | verna,2014,6.45,9.4,45078,Petrol,Dealer,Manual,0
221 | verna,2012,4.5,9.4,36000,Petrol,Dealer,Manual,0
222 | eon,2017,3.5,4.43,38488,Petrol,Dealer,Manual,0
223 | i20,2013,4.5,6.79,32000,Petrol,Dealer,Automatic,0
224 | i20,2014,6,7.6,77632,Diesel,Dealer,Manual,0
225 | verna,2015,8.25,9.4,61381,Diesel,Dealer,Manual,0
226 | verna,2013,5.11,9.4,36198,Petrol,Dealer,Automatic,0
227 | i10,2011,2.7,4.6,22517,Petrol,Dealer,Manual,0
228 | grand i10,2015,5.25,5.7,24678,Petrol,Dealer,Manual,0
229 | i10,2011,2.55,4.43,57000,Petrol,Dealer,Manual,0
230 | verna,2012,4.95,9.4,60000,Diesel,Dealer,Manual,0
231 | i20,2012,3.1,6.79,52132,Diesel,Dealer,Manual,0
232 | verna,2013,6.15,9.4,45000,Diesel,Dealer,Manual,0
233 | verna,2017,9.25,9.4,15001,Petrol,Dealer,Manual,0
234 | elantra,2015,11.45,14.79,12900,Petrol,Dealer,Automatic,0
235 | grand i10,2013,3.9,5.7,53000,Diesel,Dealer,Manual,0
236 | grand i10,2015,5.5,5.7,4492,Petrol,Dealer,Manual,0
237 | verna,2017,9.1,9.4,15141,Petrol,Dealer,Manual,0
238 | eon,2016,3.1,4.43,11849,Petrol,Dealer,Manual,0
239 | creta,2015,11.25,13.6,68000,Diesel,Dealer,Manual,0
240 | verna,2013,4.8,9.4,60241,Petrol,Dealer,Manual,0
241 | eon,2012,2,4.43,23709,Petrol,Dealer,Manual,0
242 | verna,2012,5.35,9.4,32322,Diesel,Dealer,Manual,0
243 | xcent,2015,4.75,7.13,35866,Petrol,Dealer,Manual,1
244 | xcent,2014,4.4,7.13,34000,Petrol,Dealer,Manual,0
245 | i20,2016,6.25,7.6,7000,Petrol,Dealer,Manual,0
246 | verna,2013,5.95,9.4,49000,Diesel,Dealer,Manual,0
247 | verna,2012,5.2,9.4,71000,Diesel,Dealer,Manual,0
248 | i20,2012,3.75,6.79,35000,Petrol,Dealer,Manual,0
249 | verna,2015,5.95,9.4,36000,Petrol,Dealer,Manual,0
250 | i10,2013,4,4.6,30000,Petrol,Dealer,Manual,0
251 | i20,2016,5.25,7.6,17000,Petrol,Dealer,Manual,0
252 | creta,2016,12.9,13.6,35934,Diesel,Dealer,Manual,0
253 | city,2013,5,9.9,56701,Petrol,Dealer,Manual,0
254 | brio,2015,5.4,6.82,31427,Petrol,Dealer,Automatic,0
255 | city,2014,7.2,9.9,48000,Diesel,Dealer,Manual,0
256 | city,2013,5.25,9.9,54242,Petrol,Dealer,Manual,0
257 | brio,2012,3,5.35,53675,Petrol,Dealer,Manual,0
258 | city,2016,10.25,13.6,49562,Petrol,Dealer,Manual,0
259 | city,2015,8.5,13.6,40324,Petrol,Dealer,Manual,0
260 | city,2015,8.4,13.6,25000,Petrol,Dealer,Manual,0
261 | amaze,2014,3.9,7,36054,Petrol,Dealer,Manual,0
262 | city,2016,9.15,13.6,29223,Petrol,Dealer,Manual,0
263 | brio,2016,5.5,5.97,5600,Petrol,Dealer,Manual,0
264 | amaze,2015,4,5.8,40023,Petrol,Dealer,Manual,0
265 | jazz,2016,6.6,7.7,16002,Petrol,Dealer,Manual,0
266 | amaze,2015,4,7,40026,Petrol,Dealer,Manual,0
267 | jazz,2017,6.5,8.7,21200,Petrol,Dealer,Manual,0
268 | amaze,2014,3.65,7,35000,Petrol,Dealer,Manual,0
269 | city,2016,8.35,9.4,19434,Diesel,Dealer,Manual,0
270 | brio,2017,4.8,5.8,19000,Petrol,Dealer,Manual,0
271 | city,2015,6.7,10,18828,Petrol,Dealer,Manual,0
272 | city,2011,4.1,10,69341,Petrol,Dealer,Manual,0
273 | city,2009,3,10,69562,Petrol,Dealer,Manual,0
274 | city,2015,7.5,10,27600,Petrol,Dealer,Manual,0
275 | jazz,2010,2.25,7.5,61203,Petrol,Dealer,Manual,0
276 | brio,2014,5.3,6.8,16500,Petrol,Dealer,Manual,0
277 | city,2016,10.9,13.6,30753,Petrol,Dealer,Automatic,0
278 | city,2015,8.65,13.6,24800,Petrol,Dealer,Manual,0
279 | city,2015,9.7,13.6,21780,Petrol,Dealer,Manual,0
280 | jazz,2016,6,8.4,4000,Petrol,Dealer,Manual,0
281 | city,2014,6.25,13.6,40126,Petrol,Dealer,Manual,0
282 | brio,2015,5.25,5.9,14465,Petrol,Dealer,Manual,0
283 | city,2006,2.1,7.6,50456,Petrol,Dealer,Manual,0
284 | city,2014,8.25,14,63000,Diesel,Dealer,Manual,0
285 | city,2016,8.99,11.8,9010,Petrol,Dealer,Manual,0
286 | brio,2013,3.5,5.9,9800,Petrol,Dealer,Manual,0
287 | jazz,2016,7.4,8.5,15059,Petrol,Dealer,Automatic,0
288 | jazz,2016,5.65,7.9,28569,Petrol,Dealer,Manual,0
289 | amaze,2015,5.75,7.5,44000,Petrol,Dealer,Automatic,0
290 | city,2015,8.4,13.6,34000,Petrol,Dealer,Manual,0
291 | city,2016,10.11,13.6,10980,Petrol,Dealer,Manual,0
292 | amaze,2014,4.5,6.4,19000,Petrol,Dealer,Manual,0
293 | brio,2015,5.4,6.1,31427,Petrol,Dealer,Manual,0
294 | jazz,2016,6.4,8.4,12000,Petrol,Dealer,Manual,0
295 | city,2010,3.25,9.9,38000,Petrol,Dealer,Manual,0
296 | amaze,2014,3.75,6.8,33019,Petrol,Dealer,Manual,0
297 | city,2015,8.55,13.09,60076,Diesel,Dealer,Manual,0
298 | city,2016,9.5,11.6,33988,Diesel,Dealer,Manual,0
299 | brio,2015,4,5.9,60000,Petrol,Dealer,Manual,0
300 | city,2009,3.35,11,87934,Petrol,Dealer,Manual,0
301 | city,2017,11.5,12.5,9000,Diesel,Dealer,Manual,0
302 | brio,2016,5.3,5.9,5464,Petrol,Dealer,Manual,0
303 | 


--------------------------------------------------------------------------------
/index.html:
--------------------------------------------------------------------------------
  1 | <!doctype html>
  2 | <html lang="en">
  3 |   <head>
  4 |     <meta charset="utf-8">
  5 |     <meta name="viewport" content="width=device-width, initial-scale=1">
  6 | 
  7 |     <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta2/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BmbxuPwQa2lc/FVzBcNJ7UAyJxM6wuqIj61tLrc4wSX0szH/Ev+nYRRuWlolflfl" crossorigin="anonymous">
  8 | 	
  9 |     <style>
 10 |         html {
 11 |             height: auto;
 12 |         }
 13 |     body {
 14 |         margin:0;
 15 |         padding:0;
 16 |         font-family: sans-serif;
 17 |         background: linear-gradient(#141e30, #243b55);
 18 |     }
 19 |     label,h5,h6{
 20 |         color: white;
 21 |     }   
 22 |     </style>
 23 | 
 24 |     <script>
 25 |     function validateForm() {
 26 |       var Year =document.getElementById("Year").value;
 27 |       if (Year<0)
 28 |       {
 29 |           alert("Year of purchase can't be negative");
 30 |           return false;
 31 |       }
 32 |       var Present_Price =document.getElementById("Present_Price").value;
 33 |       if (Present_Price<0)
 34 |       {
 35 |           alert("Present Price can't be negative");
 36 |           return false;
 37 |       }
 38 |       var Kms_Driven =document.getElementById("Kms_Driven").value;
 39 |       if (Kms_Driven<0)
 40 |       {
 41 |           alert("Kilometers Driven can't be negative");
 42 |           return false;
 43 |       }
 44 |       var Owner =document.getElementById("Owner").value;
 45 |       if (Owner == 0 | Owner ==1 | Owner == 3)
 46 |       {
 47 |           
 48 |       }
 49 |       else{
 50 |         alert("Owner value is invalid");
 51 |         return false;
 52 |       }
 53 |       
 54 |       
 55 |     }
 56 |     </script>
 57 | 
 58 |     <title>Car Price Prediction</title>
 59 |   </head>
 60 |   <body>
 61 |     <br/>
 62 |     <h1  style="color: blanchedalmond; text-align: center;">CAR PRICE PREDICTION</h1>
 63 |     <h5  style="color: blanchedalmond; text-align: center;">Machine Learning Model</h5>
 64 |     <div class="container" style="min-width: 50%; max-width: max-content;">
 65 |         <form action="{{ url_for('predict')}}" method="post" class="login-box" onsubmit="return validateForm()">
 66 |             
 67 |             <br/>
 68 |             <h6>Year of purchase</h6>
 69 |             <input type="number" class="form-control" id="Year" name="Year" required="required">  
 70 |             <br/>
 71 |             <h6>What is the Showroom Price?<b>(In Lakhs)</b></h6>
 72 |             <input type="number" class="form-control" id="Present_Price" placeholder="For example, Put 5 for 5L" name="Present_Price" required="required">  
 73 |             <br/>
 74 |             <h6>How Many Kilometers Drived?</h6>
 75 |             <input type="number" class="form-control" id="Kms_Driven" name="Kms_Driven" required="required">  
 76 |             <br/>
 77 |             <h6>How much owners previously the car had?</h6>
 78 |             <input type="number" class="form-control" id="Owner" name="Owner" placeholder="0 or 1 or 3" required="required">  
 79 |             <br/>
 80 |             <h6>What is the Fuel type?</h6>
 81 |             <select class="form-select form-select-md mb-3" aria-label=".form-select-lg example" name="Fuel_Type_Petrol">
 82 |                 <option value="Petrol">Petrol</option>
 83 |                 <option value="Diesel">Diesel</option>
 84 |                 <option value="Diesel">CNG</option>
 85 |             </select>
 86 |             <h6>Are you A Dealer or Individual?</h6>
 87 |             <select class="form-select form-select-md mb-3" aria-label=".form-select-lg example" name="Seller_Type_Individual">
 88 |                 <option value="Dealer">Dealer</option>
 89 |                 <option value="Individual">Individual</option>
 90 |             </select>
 91 |             <h6>Transmission type</h6>
 92 |             <select class="form-select form-select-md mb-3" aria-label=".form-select-lg example" name="Transmission_Mannual">
 93 |                 <option value="Mannual">Manual Car</option>
 94 |                 <option value="Automatic">Automatic Car</option>
 95 |             </select>
 96 |             <div style="text-align: center;" class="d-grid gap-2 d-md-block">
 97 |               <button class="btn btn-primary"  type="submit">Calculate Selling Price</button>
 98 |             </div>
 99 |           
100 |             <div class="p-2 w-full text-center">
101 |                 <h1 class="sm:text-3xl text-2xl font-large title-font mb-4 text-white-900", style="color: blanchedalmond;">{{ prediction_text }}</h1>
102 |             </div>    
103 |             </form>
104 |     </div>
105 |   </body>
106 | </html>


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/model.pkl:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Rohanvp07/Car-Price-Prediction/da17c8671439ba6de32949f6c04dd483c170ae3f/model.pkl


--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
 1 | Flask==1.1.1
 2 | gunicorn==19.9.0
 3 | itsdangerous==1.1.0
 4 | Jinja2==2.10.1
 5 | MarkupSafe==1.1.1
 6 | Werkzeug==0.15.5
 7 | numpy>=1.9.2
 8 | scipy>=0.15.1
 9 | scikit-learn>=0.18
10 | matplotlib>=1.4.3
11 | pandas>=0.19
12 | 


--------------------------------------------------------------------------------
/templates/index.html:
--------------------------------------------------------------------------------
  1 | <!doctype html>
  2 | <html lang="en">
  3 |   <head>
  4 |     <meta charset="utf-8">
  5 |     <meta name="viewport" content="width=device-width, initial-scale=1">
  6 | 
  7 |     <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta2/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-BmbxuPwQa2lc/FVzBcNJ7UAyJxM6wuqIj61tLrc4wSX0szH/Ev+nYRRuWlolflfl" crossorigin="anonymous">
  8 | 	
  9 |     <style>
 10 |         html {
 11 |             height: auto;
 12 |         }
 13 |     body {
 14 |         margin:0;
 15 |         padding:0;
 16 |         font-family: sans-serif;
 17 |         background: linear-gradient(#141e30, #243b55);
 18 |     }
 19 |     label,h5,h6{
 20 |         color: white;
 21 |     }   
 22 |     </style>
 23 | 
 24 |     <script>
 25 |     function validateForm() {
 26 |       var Year =document.getElementById("Year").value;
 27 |       if (Year<0)
 28 |       {
 29 |           alert("Year of purchase can't be negative");
 30 |           return false;
 31 |       }
 32 |       var Present_Price =document.getElementById("Present_Price").value;
 33 |       if (Present_Price<0)
 34 |       {
 35 |           alert("Present Price can't be negative");
 36 |           return false;
 37 |       }
 38 |       var Kms_Driven =document.getElementById("Kms_Driven").value;
 39 |       if (Kms_Driven<0)
 40 |       {
 41 |           alert("Kilometers Driven can't be negative");
 42 |           return false;
 43 |       }
 44 |       var Owner =document.getElementById("Owner").value;
 45 |       if (Owner == 0 | Owner ==1 | Owner == 3)
 46 |       {
 47 |           
 48 |       }
 49 |       else{
 50 |         alert("Owner value is invalid");
 51 |         return false;
 52 |       }
 53 |       
 54 |       
 55 |     }
 56 |     </script>
 57 | 
 58 |     <title>Car Price Prediction</title>
 59 |   </head>
 60 |   <body>
 61 |     <br/>
 62 |     <h1  style="color: blanchedalmond; text-align: center;">CAR PRICE PREDICTION</h1>
 63 |     <h5  style="color: blanchedalmond; text-align: center;">Machine Learning Model</h5>
 64 |     <div class="container" style="min-width: 50%; max-width: max-content;">
 65 |         <form action="{{ url_for('predict')}}" method="post" class="login-box" onsubmit="return validateForm()">
 66 |             
 67 |             <br/>
 68 |             <h6>Year of purchase</h6>
 69 |             <input type="number" class="form-control" id="Year" name="Year" required="required">  
 70 |             <br/>
 71 |             <h6>What is the Showroom Price?<b>(In Lakhs)</b></h6>
 72 |             <input type="number" class="form-control" id="Present_Price" placeholder="For example, Put 5 for 5L" name="Present_Price" required="required">  
 73 |             <br/>
 74 |             <h6>How Many Kilometers Drived?</h6>
 75 |             <input type="number" class="form-control" id="Kms_Driven" name="Kms_Driven" required="required">  
 76 |             <br/>
 77 |             <h6>How much owners previously the car had?</h6>
 78 |             <input type="number" class="form-control" id="Owner" name="Owner" placeholder="0 or 1 or 3" required="required">  
 79 |             <br/>
 80 |             <h6>What is the Fuel type?</h6>
 81 |             <select class="form-select form-select-md mb-3" aria-label=".form-select-lg example" name="Fuel_Type_Petrol">
 82 |                 <option value="Petrol">Petrol</option>
 83 |                 <option value="Diesel">Diesel</option>
 84 |                 <option value="Diesel">CNG</option>
 85 |             </select>
 86 |             <h6>Are you A Dealer or Individual?</h6>
 87 |             <select class="form-select form-select-md mb-3" aria-label=".form-select-lg example" name="Seller_Type_Individual">
 88 |                 <option value="Dealer">Dealer</option>
 89 |                 <option value="Individual">Individual</option>
 90 |             </select>
 91 |             <h6>Transmission type</h6>
 92 |             <select class="form-select form-select-md mb-3" aria-label=".form-select-lg example" name="Transmission_Mannual">
 93 |                 <option value="Mannual">Manual Car</option>
 94 |                 <option value="Automatic">Automatic Car</option>
 95 |             </select>
 96 |             <div style="text-align: center;" class="d-grid gap-2 d-md-block">
 97 |               <button class="btn btn-primary"  type="submit">Calculate Selling Price</button>
 98 |             </div>
 99 |           
100 |             <div class="p-2 w-full text-center">
101 |                 <h1 class="sm:text-3xl text-2xl font-large title-font mb-4 text-white-900", style="color: blanchedalmond;">{{ prediction_text }}</h1>
102 |             </div>    
103 |             </form>
104 |     </div>
105 |   </body>
106 | </html>
107 | 


--------------------------------------------------------------------------------