Startup_Business_Profit_Prediction.ipynb

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    "\n",
    "<h1 style=\"color:DarkOliveGreen;\"> Startup Business Profit Prediction </h1>"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<h2 style=\"color:Peru;\"> Importing Required Libraries </h2>"
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   "metadata": {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wdbuqmsP2bIk"
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   "source": [
    "<h2 style=\"color:Peru;\"> Loading Dataset </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "rjNecPqL2YzD"
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   "outputs": [],
   "source": [
    "df = pd.read_csv('./50_Startups.csv')"
   ]
  },
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   "execution_count": 5,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>Florida</td>\n",
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       "      <th>3</th>\n",
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       "      <td>New York</td>\n",
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      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94"
      ]
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     "execution_count": 5,
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   "source": [
    "# lets check the first five rows of dataset\n",
    "df.head()"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   R&D Spend        50 non-null     float64\n",
      " 1   Administration   50 non-null     float64\n",
      " 2   Marketing Spend  50 non-null     float64\n",
      " 3   State            50 non-null     object \n",
      " 4   Profit           50 non-null     float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 2.1+ KB\n"
     ]
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   "source": [
    "df.info()"
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       "      <td>112012.639200</td>\n",
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       "      <th>25%</th>\n",
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      "text/plain": [
       "           R&D Spend  Administration  Marketing Spend         Profit\n",
       "count      50.000000       50.000000        50.000000      50.000000\n",
       "mean    73721.615600   121344.639600    211025.097800  112012.639200\n",
       "std     45902.256482    28017.802755    122290.310726   40306.180338\n",
       "min         0.000000    51283.140000         0.000000   14681.400000\n",
       "25%     39936.370000   103730.875000    129300.132500   90138.902500\n",
       "50%     73051.080000   122699.795000    212716.240000  107978.190000\n",
       "75%    101602.800000   144842.180000    299469.085000  139765.977500\n",
       "max    165349.200000   182645.560000    471784.100000  192261.830000"
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "# summary statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V8FDjezJ2s6n"
   },
   "source": [
    "<h3 style=\"color:green;\"> Data Description </h3>\n",
    "\n",
    "The data collected from New York, California and Florida about 50 business Startups. The independent variables used in this dataset are R&D spending, Administration and Marketing Spending.<br>\n",
    "The dependent variable is Profit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ULTQ5OpA2wHd"
   },
   "source": [
    "<h2 style=\"color:Peru;\"> Exploratory Data Analysis </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6CD4O-LK2ylL"
   },
   "source": [
    "- Find Unwanted Columns\n",
    "- Find Missing Values\n",
    "- Find Features with one value\n",
    "- Explore the Categorical Features\n",
    "- Find Categorical Feature Distribution\n",
    "- Relationship between Categorical Features and Label\n",
    "- Explore the Numerical Features\n",
    "- Find Discrete Numerical Features\n",
    "- Relation between Discrete numerical Features and Labels\n",
    "- Find Continous Numerical Features\n",
    "- Distribution of Continous Numerical Features\n",
    "- Relation between Continous numerical Features and Labels\n",
    "- Find Outliers in numerical features\n",
    "- Explore the Correlation between numerical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CYH6OI2222BS"
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   "source": [
    "**1. Find unwanted Columns**"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "38jMkTJk219O"
   },
   "source": [
    "**Insight**:\n",
    "- There is no unwanted column present in given dataset to remove"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W4U_fw0c2160"
   },
   "source": [
    "**2. Find Missing Values**"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "R&D Spend          0\n",
       "Administration     0\n",
       "Marketing Spend    0\n",
       "State              0\n",
       "Profit             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uzRgVUtq21tP"
   },
   "source": [
    "**Insight**:\n",
    "- No missing value found"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7qAEavr921er"
   },
   "source": [
    "**3. Find Features with one value**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-KyumQT22kC5",
    "outputId": "b00bdc0f-f5f3-4368-f44f-696f2902b476"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Administration': 50,\n",
       " 'Marketing Spend': 48,\n",
       " 'Profit': 50,\n",
       " 'R&D Spend': 49,\n",
       " 'State': 3}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_values = {col:df[col].nunique() for col in df.columns}\n",
    "feature_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KBeiO26Y3PR_"
   },
   "source": [
    "**Insight**:\n",
    "- no feature found with one value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "etk1klD93TIM"
   },
   "source": [
    "**4. Explore the Categorical Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VzWLOY1r3Iyf",
    "outputId": "18ef172f-da8e-41a4-d95e-af01200a43c6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['State']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features = [feature for feature in df.columns if df[feature].dtypes=='O']\n",
    "categorical_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9vzB3KCw4iUW"
   },
   "source": [
    "**Insight**:\n",
    "- only one feature that is State is categorical and it has 3 unique values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EI7o4JoR4k7N"
   },
   "source": [
    "**5. Find Categorical Feature Distribution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 457
    },
    "id": "BG9dRI6g3Vhe",
    "outputId": "464a6e0b-e298-41de-d57c-af29d13469c2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check count based on categorical features\n",
    "sns.set(style=\"whitegrid\")\n",
    "plt.figure(figsize=(12,6))\n",
    "total = float(len(df))\n",
    "ax = sns.countplot(x=\"State\", data=df)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Count Plot For States\", fontsize=20)\n",
    "for p in ax.patches:\n",
    "    percentage = '{:.1f}%'.format(100 * p.get_height()/total)\n",
    "    x = p.get_x() + p.get_width() / 2.\n",
    "    y = p.get_height()\n",
    "    ax.annotate(percentage, (x, y),ha='center',va='bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gGrXMD5y4ywC"
   },
   "source": [
    "**Insight**:\n",
    "- State with florida has less number of records in given dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YyGczSZr420N"
   },
   "source": [
    "**6. Relationship between Categorical Features and Label**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "id": "VPaBNMqx4rFQ",
    "outputId": "0c6b2bd2-d2af-428b-ea7a-dd2691d945a1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Find out the relationship between categorical variable and dependent varaible\n",
    "plt.figure(figsize=(12, 6))\n",
    "fig = sns.boxplot(x='State', y=\"Profit\", data=df.sort_values('Profit',ascending=False))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m78BWX3L49-d"
   },
   "source": [
    "**Insight**:\n",
    "- It seems startup in state of California has less profits than others two states."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IBv8Y51a5A7d"
   },
   "source": [
    "**7. Explore the Numerical Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c5NMjABp47hZ",
    "outputId": "698f8092-91c2-4588-dde6-98fa0c0bace1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical variables:  ['R&D Spend', 'Administration', 'Marketing Spend']\n"
     ]
    }
   ],
   "source": [
    "# list of numerical variables\n",
    "numerical_features = [feature for feature in df.columns if ((df[feature].dtypes != 'O') & (feature not in ['Profit']))]\n",
    "print('Numerical variables: ', numerical_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gC_O1qpy5IGp"
   },
   "source": [
    "**Insight**:\n",
    "- there are three numerical features that are R&D Spend, Administration and Marketing Spend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RoAfLmWX5Lb7"
   },
   "source": [
    "**8. Find Discrete Numerical Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_J7rND_f5DWC",
    "outputId": "e4428e97-83b7-4afc-aa72-88bc0a1a541b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete Variables Count: 0\n"
     ]
    }
   ],
   "source": [
    "discrete_feature = [feature for feature in numerical_features if df[feature].nunique()<25]\n",
    "print(\"Discrete Variables Count: {}\".format(len(discrete_feature)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1to02gJA5Qwt"
   },
   "source": [
    "**Insight**:\n",
    "- no discrete variable found"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oA0VZ5H65TrF"
   },
   "source": [
    "**9. Find Continous Numerical Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k2WyA0Cg5GXx",
    "outputId": "bbfc7b74-f69c-4c0b-9375-a156cc6a3e05"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R&D Spend', 'Administration', 'Marketing Spend']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continuous_features = [feature for feature in numerical_features if feature not in discrete_feature + ['Profit']]\n",
    "continuous_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lHx2zM-y5Zz9"
   },
   "source": [
    "**Insight**:\n",
    "- there are three continuous numerical features - 'R&D Spend', 'Administration', 'Marketing Spend'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XyWkpqme5ckw"
   },
   "source": [
    "**10. Distribution of Continous Numerical Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 437
    },
    "id": "oCD7I9qj5Xb1",
    "outputId": "75de696a-3a3e-4998-d1bc-684670fa4363"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'family': 'serif',\n",
    "        'color':  'darkred',\n",
    "        'weight': 'normal',\n",
    "        'size': 16,\n",
    "        }\n",
    "fig,axes = plt.subplots(1,3,figsize=(20,6),sharey=True)\n",
    "fig.subplots_adjust(wspace=0.1, hspace=0.3)\n",
    "fig.suptitle('Scatter Plot Of Continous Variables',fontsize = 20, fontdict=font)\n",
    "fig.subplots_adjust(top=0.95)\n",
    "\n",
    "axes = axes.ravel()\n",
    "\n",
    "for i,col in enumerate(continuous_features):\n",
    "    #using log transformation\n",
    "    sns.distplot(df[col],ax=axes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fef8xp7t5h2n"
   },
   "source": [
    "**Insight**:\n",
    "- it seems all continuous features are normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c0w1AKDM5kcm"
   },
   "source": [
    "**11. Relation between Continuous numerical Features and Labels**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 437
    },
    "id": "JHfdtQ1e5fbG",
    "outputId": "5f86e01e-f30e-4009-a3ed-92968d4b6b9b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VFBTUeFl1paSkKDU11R3npk2bJElffvml0tLSNGDAAI0fP14nT550r+ONZdVx5MgRd9zp6elKSUlRjx49KtwuM+OfPXu2UlJS1L59ex04cMD9eEXHsq+XVWcbKmoHknXagqf97+tjpbbbAS4pLCzUjBkz1L9/fw0ZMkRPPvmkJOu1o4p88sknGjp0qNLT05WWlqY1a9ZYehv8Pa9VJ6dZLS94+gxKvPrqq2WWWWkbPMXvqT1L1j+GJM9t2Wrx+5NA2EZ/z5m1qaJc6+scZYWx2YMPPqi0tDQNHTpUo0eP1tdffy0pOI+NElf2X8F4XHiVAZ8YO3askZGRYRiGYWRkZBhjx471eQw//vijsXnzZvffzz//vPHf//3fhmEYRrt27Yy8vLwy6+Tl5Rk33XST8d133xmGYRiPPfaYMX/+/Botq4mf//znxv79+0s95nQ6jb59+xrbtm0zDMMwFixYYEyfPt1ry2rLs88+azz99NMet8vs+Ldt22ZkZWWVia2iY9nXy6qzDRW1A8OwTlvwtP99eaz4oh0Es2eeecaYNWuW4XK5DMMwjBMnThiGYb125InL5TKSkpLcx+PXX39tdO3a1XA6nZbdBn/Pa1XNaVbMC54+A8MwjL179xq//vWvSy2z2jZ4it9TezYM6x9DFbVlq8XvTwJhG/09Z9YmT7nW1znKKmOzM2fOuP+/du1aY+jQoYZhBOexYRhl+69gPS68icKVD+Tk5BiJiYlGcXGxYRiGUVxcbCQmJhonT540Na6PP/7YuOuuuwzD8PxlPTMz07j33nvdf+/evdsYNGhQjZbVRHkD3a+++sr4xS9+4f775MmTRteuXb22rDYUFhYaycnJxt69ez1ul1Xivzy2io5lXy+r7jZc6fJ2YBjWawuVLVz5YzsIZnl5eUZiYmKZY83K7ehKLpfL6NGjh7F9+3bDMAxj69atRv/+/f1iG/w9r1U2p1k5L1y5DYWFhcaIESOMw4cPl1pm1W24PEZP7dkwrNumryxcldeWrRy/1QXaNvp7zvSGklzr6xxlxbHZsmXLjGHDhgXtsVFe/8VxUfvCzD7jKxg4HA41a9ZMoaGhkqTQ0FDFxcXJ4XAoNjbWlJhcLpcWL16slJQU92Njx46V0+nUrbfeqkmTJikiIkIOh0MtWrRwP6dFixZyOBySVO1lNTVt2jQZhqHExERNnTq1zHvFxsbK5XLp1KlTXlkWExNT421Yv369mjVrpuuvv97jdjVo0MBy8Vd0LBuG4dNltdF2ymsHkvXbgq+OFW+3g2B2+PBhxcTE6NVXX9WWLVsUFRWlyZMnq06dOn7TjkJCQvTSSy/pwQcfVL169ZSfn68//elPPs8TNc0FgZTXrsxp/pQXXn75ZaWlpally5alHveHbfDUnpOSkvyiPXhqy5Lv24dZ4+LaZsWxf20JpJxZXZfnWl/nKCuNzR5//HF9/vnnMgxDb7/9dtAeG+X1X8F8XHgLc1wFqWeeeUb16tXTmDFjJEkbNmzQ0qVLtWjRIn377bdasGCByRGWb9GiRfrwww/1wQcfyDAMzZw50+yQquWDDz7QHXfc4f47ULbL31zZDiTrtwWOlcDgdDp1+PBhderUSUuXLtW0adM0adIknTt3zuzQKq24uFhvvvmmXnvtNX3yySd6/fXX9cgjj/jVNgSa8nKaP9i1a5f27t2r0aNHmx1KtXhqz3l5eWaHVime2nJ+fr7ZoQGW5K+5trbNmjVLGzZs0JQpUzRnzhyzwzGFv/df/oTClQ/Y7XYdP35cTqdT0sUBTnZ2tux2uynxzJ49W4cOHdJLL70km83mjlGSoqOjNXz4cO3cudP9eFZWlnvdrKws93Oru6wmSl4jIiJCo0eP1s6dO8u8V25urmw2m2JiYryyrKaOHz+ubdu2aciQIRVuV8njVoq/omPZ18tqqrx2ULKNknXbgi+PFW+2g2Bnt9sVFhamwYMHS5JuvPFGNWrUSHXq1PGbdvT1118rOztbiYmJkqTExETVrVtXkZGRfrMNUuDkNU99uz/khW3btungwYO67bbblJKSomPHjunXv/61PvvsM7/YBk/t+bvvvvOLY8hTWz548KBfxG9FgbyNgZIzq+vKXOvrHGXFsdnQoUO1ZcsWNW/ePOiODU/916FDh4L+uKhtFK58oHHjxurYsaNWrlwpSVq5cqU6duxoyumtL774ovbu3asFCxYoIiJCknT69GmdP39e0sVf3VavXq2OHTtKkm655Rbt2bPHfYeG9957TwMHDqzRsuo6d+6czp49K0kyDEOZmZnq2LGjOnfurPPnz2v79u3u90pNTZUkryyrqWXLlql3795q1KhRhdtlxfgrOpZ9vawmymsHkvXbgq+PFW+2g2AXGxur5ORkff7555Iu3hXn5MmTatOmjd+0o+bNm+vYsWP6z3/+I0k6ePCgTp48qWuuucZvtkEKjLzmKaf5S16499579dlnn2n9+vVav369mjdvroULF6pXr15+sQ2e2vM111zjF8eQp7bcunVrv4jfigJ5GwMhZ1ZXebnW1znKCmOz/Pz8UtNerF+/Xg0bNgzKY8NT/zVhwoSgOy68zpsTaOGSb7/91rjzzjuN/v37G3feeadx8OBBn8dw4MABo127dkb//v2NtLQ0Iy0tzXjwwQeNnTt3GoMHDzaGDBliDBw40HjsscdKTTC6du1ao3///kbfvn2NSZMmGfn5+TVeVh0//PCDkZ6ebgwePNgYNGiQMWnSJOP48eOGYRjGjh07jMGDBxv9+vUz7r777lJ38/HGspro37+/sXHjxkptl5nxP/PMM8Ytt9xidOzY0bjpppvcE4pXdCz7ell1tsFTOzAMw1JtobzYzThWvNUOcLHtjxkzxhg8eLAxdOhQY8OGDYZhWK8dVWT58uXuNjNkyBBj7dq1lt4Gf89rVc1phmG9vODpM7jclRO3W2kbPMXvqT0bhvWPIcPw3JatFr8/CYRt9PecWZsqyrW+zlFmj81OnDhhDB8+3Bg8eLCRlpZmjB071n3DqWA8Ni53ef8VbMeFt4UYhmGYXTwDAAAAAAAArsSlggAAAAAAALAkClcAAAAAAACwJApXAAAAAAAAsCQKVwAAAAAAALAkClcAAAAAAACwJApXALxu7NixWrJkidlhAIDPTZ8+XfPmzavWum+88YYef/zxWn+uN0yYMEHLli0z7f0BwMqOHDmi9u3bq7i4uMav9eGHH2r8+PG1EJV1zZ8/X9OmTTM7DFgIhSvAy1JSUnTDDTcoISFBN998s6ZPn678/PxSz/nss8+UmpqqhIQEpaWlad++faWWb9myRR06dFBCQoISEhJ06623avLkydq9e3eF771kyRL369500036zW9+o7y8vFrfRgAIRmPHjlX37t114cIFr7z+/fffr1mzZtXqc2vjh4TyvlC8/fbbGjZsWI1eFwDMlpKSos6dOys3N7fU40OHDlX79u115MgRn8ZTXsErLS1Nf/7zn73yftu3b9fIkSOVmJioHj16aOTIkVf9vgH4AoUrwAfeeOMN7dq1SxkZGdq3b5/+9Kc/lVo+ffp0jR8/Xjt37tQLL7ygBg0alHmNuLg47dq1Szt37tT777+vn/zkJ/rVr36lL774otz33Lp1q+bNm6cXX3xRu3btUmZmpgYNGuSV7QOAYHPkyBFt375dISEh+uc//2l2OLWmNs4GAAB/Fh8fr1WrVrn/3r9/vwoKCqr9ev6SV/Py8nT//fdrzJgx2rp1qz799FNNnDhRERERZocGULgCfKlp06bq1auXvv7661KPh4WFKT4+XiEhIbruuuvUsmVLj68REhKi5s2ba/LkyRo+fLj++Mc/lvu8PXv2qGvXrurUqZMkKSYmRsOGDVN0dLSki8Wyp556Svfcc48SEhI0ZswYHT161L3+wYMHdc8996hHjx4aMGCAMjMz3cumT5+up59+Wvfee68SEhI0fPhw/fDDD+7ln3/+uVJTU5WYmKiZM2fKMIyq7ywAsLCMjAzdeOONGjZsmDIyMtyP79u3T8OGDVNCQoIeeeQRFRYWupdt2bJFt956q9566y317NlTvXr10rp167Rx40YNGDBAPXr00BtvvOF+/uVnNpX86r5s2TL16dNHycnJev3118t9bmFhoaZNm6bk5GQlJSXpjjvuUE5OjubNm6ft27dr5syZSkhI0MyZMyVJ7du316JFi9S/f3/1799fkvTss8+qd+/e6tatm26//XZt375dkvTpp5/qzTff1EcffeQ+S1gqfSaXy+XSa6+9pp///Ofq2bOnfve73+ns2bOV2g4AMFt6enqpvJ6RkaGhQ4eWes6GDRs0dOhQdevWTb1799b8+fPdy0ry3JIlS9SnTx/dddddZd5j9erVSklJ0YEDB+RyufSnP/1Jffv2VXJysiZPnqxTp05JksaMGSNJ6t69uxISErRr1y4tXbpUo0aNcr9W+/bttXjxYvXv319JSUl6+umn3WNvp9Op559/XsnJyUpJSdHf/vY3j5csfvfdd5KkwYMHKzQ0VHXq1FGvXr3UoUMHSdLSpUs1cuRIzZw5U4mJiUpNTS31A/rZs2f12GOPqVevXrrllls0b948OZ1O97qjRo3S7Nmz1b17d6WkpGjjxo3udQ8fPqwxY8YoISFB99xzj3788cfKfFQIIhSuAB86duyYNm3apNatW7sfMwxDN9xwg5544okqn37cr18/7du3T+fOnSuz7MYbb9Rnn32mV155RTt27Cj3UpYVK1bowQcfdF+KWPKl59y5cxo/frwGDx6sf/3rX5o3b56efvppffvtt+51MzMzNXHiRG3btk2tW7d2z+GSm5uriRMn6pFHHtHmzZvVunVr7dy5s0rbBQBWt3z5cg0ZMkRDhgzRZ599ppycHF24cEEPPfSQ0tPTtXXrVqWmpmrNmjWl1svJyVFhYaE+/fRTPfzww3riiSf04Ycf6oMPPtCiRYv02muv6fDhwx7fd8eOHfr444/1zjvvaMGCBTp48GCZ5yxbtkx5eXnasGGDtmzZoqefflp16tTRlClTlJSUpKeeekq7du3SU0895V5n3bp1ev/9990/UnTp0kUZGRnaunWrBg8erMmTJ6uwsFC33nqr7rvvPg0cOFC7du3Shx9+WOb9ly5dqmXLlundd9/VunXrdO7cOXeRrCrbAQBm6Nq1q/Ly8nTw4EE5nU6tWrXKXaQvUbduXc2ePVvbt2/Xm2++qcWLF2vdunWlnrNt2zZlZmZq4cKFpR7/4IMPNHfuXP3lL39Ru3bt9Ne//lXr1q3T3/72N23atEkNGzZ058y//e1v7tfatWuXEhISyo15w4YN+sc//qEPP/xQH330kTZt2iRJev/99/Xpp59q+fLlWrZsWZkYL3fttdcqNDRU//Vf/6WNGzfq9OnTZZ6ze/dutW7dWps3b9bDDz+siRMnuots06dPV1hYmNasWaOMjAx9/vnnpS5N3717t6699lpt3rxZEyZM0OOPP+4usE2bNk3XX3+9tmzZogcffJA5E1EGhSvABx566CElJCSod+/eio2N1cMPP+xe9tZbb6mgoEBTpkzR3Xff7S5eLVmyRJMmTarwdePi4mQYhvuX7MslJSVp/vz52rdvn+677z4lJyfrueeec//yIUl9+vRR9+7dFRERoSlTpujLL7+Uw+HQhg0bFB8frzvuuENhYWHq1KmTBgwYoI8//ti9bt++fXXDDTcoLCxMaWlp7rPIPv30U1133XVKTU1VeHi47rrrLjVp0qRG+w8ArGT79u3KysrSwIED1blzZ7Vq1UorV67UV199paKiIt11110KDw9XamqqunTpUmrdsLAwPfDAAwoPD9egQYP0448/aty4cYqOjtZ1112nn/70p9q/f7/H9544caLq1KmjDh06qEOHDvq///u/Ms8JCwvTqVOndOjQIYWGhqpz587us209uffeexUTE6M6depIunjGQaNGjRQWFqbx48frwoUL7l/jr2bFihW6++671apVK0VFRWnq1KnKzMws9Qt/ZbYDAMxSctbV559/rrZt26pZs2allicnJ6t9+/ay2Wzq0KGDfvGLX2jr1q2lnjNp0iTVq1fPnVcl6Z133tHChQv117/+Vddcc40k6b333tOUKVPUvHlzRUREaOLEiVq9enWVLjH8zW9+owYNGqhFixZKTk5259SPPvpI48aNU/PmzdWwYUPde++9Hl8jOjpa//u//6uQkBA9+eST6tmzp+6//37l5OS4nxMbG+vu4wYNGqRrr71WGzZsUE5OjjZu3KjHHntM9erVU+PGjXX33XeXuuSyRYsWGjFihEJDQzVs2DCdOHFCOTk5ysrK0p49ezR58mRFRES4z8gCLhdmdgBAMFiwYIFuuukmbd26Vb/97W/1448/uuexevfddzV//nwlJCQoPz9f48aN07vvvqudO3fqZz/7WYWvm52drZCQENWvX7/c5b1791bv3r3lcrm0ZcsWTZ48Wddee61GjhwpSWrevLn7uVFRUWrYsKGys7N19OhR7d69W0lJSe7lTqez1K9Nlxej6tSp4z7rKzs7u9TrhoSEyG63V3ZXAYDlZWRk6Oabb1ZsbKyki5dVLFu2TE2bNlWzZs0UEhLifm6LFi1KrRsTE6PQ0FBJcn+Zady4sXt5ZGRkmRt4XO7y3Fu3bt1yz7hNT0/XsWPHNHXqVJ05c0ZpaWmaMmWKwsPDPb7ulXl64cKF+sc//uHuZ/Ly8ip96UZ2drbi4+Pdf8fHx6u4uFgnT56s0nYAgFnS09M1ZswYHTlyROnp6WWWf/XVV5o7d66++eYbFRUV6cKFC0pNTS31nMvHwyUWLlyohx56qNSyrKwsPfTQQ7LZLp1TYrPZSuXMq2natKn7/3Xr1nX3I9nZ2aXye3kxXa5t27Z6/vnnJV2cNuTRRx/VH/7wB7344ouSVG4fl52draysLBUXF6tXr17uZS6Xq9R7X5n3pYtXeZR8L6pXr16p13U4HJXefgQ+CleAD/Xo0UO33367Zs+erddee03SxQkbS35RGTVqlE6fPq1x48ZJ0lVvbb527Vp16tSpVKIvj81mU8+ePfWzn/1M33zzjfvxY8eOuf+fn5+v06dPKy4uTna7Xd27d9df/vKXKm9j06ZNS72uYRh0PAACxvnz5/XRRx/J5XLp5ptvliRduHBBZ86cUdOmTXX8+HEZhuEe2GdlZalVq1Y+jTE8PFwTJ07UxIkTdeTIEd1777269tprNXz4cI/rXP5FZPv27Xr77bf1P//zP7ruuutks9nUvXt39yUdlz+3PHFxcaXmTMzKylJYWJgaN25cqn8AAKuKj49Xy5YttXHjxnLv2Prb3/5WY8aM0dtvv63IyEjNmjWrTHG/vFz55z//WRMmTFCTJk00YMAASReLSX/4wx+UmJhY5vmX59LquHJcXpUc3LZtW91+++36+9//7n7syj7O4XAoJSXFfbbY5s2bFRZWtRJD06ZNdebMGZ07d879nSYrK+uqfQ2CC5cKAj5211136V//+pf7FN7U1FTNmTNHhw8fVnFxsW644QadOnVK4eHhcrlcZdY3DEPHjx/Xq6++qiVLlmjq1Knlvs+6deu0atUqnT59WoZhaPfu3dq6datuvPFG93M2btyo7du368KFC3r55Zd14403ym63q0+fPvr++++VkZGhoqIiFRUVaffu3ZWag6R379765ptvtGbNGhUXF+vdd98tdYoxAPizdevWKTQ0VKtWrVJGRoYyMjKUmZmppKQkrVu3TmFhYXr33XdVVFSkNWvWaM+ePT6PcfPmzdq/f7+cTqeio6MVFhbm/iW/SZMmFc6hJV38ISM0NFSxsbEqLi7Wq6++qry8PPfyxo0b6+jRo+X2UdLFM9DeeecdHT58WPn5+Zo3b54GDhxY5S8zAGCmWbNm6Z133in3B+L8/Hw1bNhQkZGR2r17t1auXFmp1/zpT3+qt99+WzNnznTfkXbUqFF66aWX3EWq3Nxc91xUsbGxstlsV83bngwcOFDvvvuujh8/rjNnzuitt97y+NyDBw/qz3/+s7u45XA4tHLlylLfHXJzc9193EcffaSDBw+qd+/eiouL080336znn39eeXl5crlc+uGHH8pcPlme+Ph4de7cWfPnz9eFCxe0fft2ffLJJ9XaXgQuCleAj8XGxio9PV0LFiyQdHEiw6SkJP3qV79S9+7dNX/+fC1YsEAdOnTQxIkTVVRUJOniqb4JCQlKSEjQHXfcoQMHDuivf/1rqVNyL9ewYUO9//776t+/v7p166ZHH31Uv/71r0td7jd48GAtWLBAycnJ+ve//+2+Q2F0dLQWLlyozMxM3XLLLerVq5fmzp1b7gTv5W3fyy+/rBdeeEHJyck6dOiQunXrVtPdBgCWsGzZMt1+++1q0aKFmjZt6v73q1/9SqtWrdL8+fO1bNky9ejRQ5mZmerXr5/PY8zJydHDDz+sxMREDRo0SD169HBf6jJu3DitXr1a3bt317PPPlvu+iV3hBowYIBSUlIUGRlZ6nKPksthkpOTNWzYsDLr33HHHUpLS9OYMWN02223KSIiQk8++aQXthQAvKd169Zl5iksMWPGDL3yyitKSEjQggULNHDgwEq/bocOHfTGG2/oySef1MaNGzVu3DilpKRo/PjxSkhI0IgRI7R7925JFy+pu//++zVq1CglJSXpyy+/rNI2jBgxQjfffLPS0tI0dOhQ9e7dW2FhYe5L1i8XHR2tr776SsOHD1fXrl01YsQItWvXTtOnT3c/54YbbtChQ4f0s5/9TC+99JJeeeUVNWrUSJI0Z84cFRUVadCgQerevbsefvhhnThxolJxvvDCC/rqq6+UnJysBQsWlLmLIxBicJ96IChNnz5dzZo105QpU8wOBQAAAICXbdy4Ub///e+rdUbT0qVLtWTJEi1evKHlwz0AAACZSURBVNgLkQEV44wrAAAAAAACzPnz57Vx40YVFxfr+PHjWrBggfr27Wt2WECVUbgCAAAAACDAGIahV155Rd27d9fQoUPVtm1bTZ482eywgCrjUkEAAAAAAABYEmdcAQAAAAAAwJIoXAEAAAAAAMCSKFwBAAAAAADAkihcAQAAAAAAwJIoXAEAAAAAAMCSKFwBAAAAAADAkv4f1gF50DRx04kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(1,3,figsize=(20,6),sharey=True)\n",
    "fig.subplots_adjust(wspace=0.1, hspace=0.3)\n",
    "fig.suptitle('Scatter Plot Of Continuous Variables vs Profit',fontsize = 20, fontdict=font)\n",
    "fig.subplots_adjust(top=0.95)\n",
    "\n",
    "axes = axes.ravel()\n",
    "\n",
    "for i,col in enumerate(continuous_features):\n",
    "    x = df[col]\n",
    "    y = df['Profit']\n",
    "    sns.scatterplot(x, y ,ax=axes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v9pgzVb05p-2"
   },
   "source": [
    "**Insight**:\n",
    "- it seems R&D Spend and Marketing Spend features linearly progressing with Profit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jtzra2ib5v8k"
   },
   "source": [
    "**12. Find Outliers in numerical features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 339
    },
    "id": "TnJqRiv45nAq",
    "outputId": "ba0be47e-0e8d-4775-f3ee-5564d860f817"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot on numerical features to find outliers\n",
    "fig, ax = plt.subplots(1, 3, figsize=(20, 5))\n",
    "for variable, subplot in zip(numerical_features, ax.flatten()):\n",
    "    sns.boxplot(df[variable], ax=subplot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yhHnXY2D53NW"
   },
   "source": [
    "**Insight**:\n",
    "- it seems no outliers found"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ujsw9rpi56g3"
   },
   "source": [
    "**13. Explore the Correlation between numerical features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 464
    },
    "id": "5gEZiHU_5z6v",
    "outputId": "284a5fcf-9e9f-421e-800b-9140eb4742bd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking for correlation\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(df.corr(), annot=True, cmap='plasma')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jLkUQ16u5_rJ"
   },
   "source": [
    "**Insight**:\n",
    "- it seems R&D Spend and marketing spend is correlated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pXCx3CB75_dk"
   },
   "source": [
    "<h2 style=\"color:Peru;\"> Feature Engineering </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D8Qc_DG06DH_"
   },
   "source": [
    "- Drop unwanted Features\n",
    "- Handle Missing Values\n",
    "- Handle Categorical Features\n",
    "- Handle Feature Scalling\n",
    "- Remove Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lutpHazk6GGD"
   },
   "source": [
    "**As per Exploratory Data Analysis EDA**, \n",
    "- There is no unwanted column present in given dataset to remove\n",
    "- No missing value found\n",
    "- Only one feature that is State is categorical and it has 3 unique values\n",
    "- No outliers found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "Pa85X2zY59WB"
   },
   "outputs": [],
   "source": [
    "# Handling Categorical Features\n",
    "df_state_dummies = pd.get_dummies(df['State'],prefix='state',drop_first=True)\n",
    "# concat df and state dummies\n",
    "df = pd.concat([df,df_state_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "OhiPcMoG6LuC",
    "outputId": "2b764d86-14cd-4731-f82e-f8e740fee80e"
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       "      <th>R&amp;D Spend</th>\n",
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       "   R&D Spend  Administration  Marketing Spend       State     Profit  \\\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83   \n",
       "1  162597.70       151377.59        443898.53  California  191792.06   \n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39   \n",
       "3  144372.41       118671.85        383199.62    New York  182901.99   \n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94   \n",
       "\n",
       "   state_Florida  state_New York  \n",
       "0              0               1  \n",
       "1              0               0  \n",
       "2              1               0  \n",
       "3              0               1  \n",
       "4              1               0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Ogdx3Pna6OCx"
   },
   "outputs": [],
   "source": [
    "# drop the original categorical columns\n",
    "df.drop(['State'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6cgYpXbB6Qo5",
    "outputId": "5c2e0129-a6f7-4211-91e6-f8e9527b32fb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 6)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FfgQ5oiD6UUF"
   },
   "source": [
    "<h2 style=\"color:Peru;\"> Model Selection </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "t0l28U6P6SCR"
   },
   "outputs": [],
   "source": [
    "# Separate Dependent and Independent Variables\n",
    "X = df.drop('Profit',axis=1)\n",
    "y = df['Profit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "MsfFG6kl6Wfa"
   },
   "outputs": [],
   "source": [
    "# Train-Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "tIxpXLxN6Ycq"
   },
   "outputs": [],
   "source": [
    "# import sklearn libraries for model building\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "q0XcewFC6tBX"
   },
   "outputs": [],
   "source": [
    "# Hyper parameter tuning\n",
    "def find_best_model_using_gridsearchcv(X,y):\n",
    "    algos = {\n",
    "        'linear_regression' : {\n",
    "            'model': LinearRegression(),\n",
    "            'params': {\n",
    "                'fit_intercept': [True, False],\n",
    "                'normalize': [True, False],\n",
    "                'copy_X': [True, False]\n",
    "            }\n",
    "        },\n",
    "        'lasso': {\n",
    "            'model': Lasso(),\n",
    "            'params': {\n",
    "                'alpha': [1,2],\n",
    "                'selection': ['random', 'cyclic']\n",
    "            }\n",
    "        },\n",
    "        'decision_tree': {\n",
    "            'model': DecisionTreeRegressor(),\n",
    "            'params': {\n",
    "                'criterion' : ['mse','friedman_mse'],\n",
    "                'splitter': ['best','random']\n",
    "            }\n",
    "        },\n",
    "        'random_forest': {\n",
    "            'model': RandomForestRegressor(),\n",
    "            'params': {\n",
    "                'n_estimators' : [int(x) for x in np.linspace(start = 100, stop = 400, num = 100)],\n",
    "                'max_features': ['auto', 'sqrt'],\n",
    "                'max_depth':[int(x) for x in np.linspace(10, 31, num = 11)],\n",
    "                'min_samples_leaf':[1, 2]\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    scores = []\n",
    "    cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=50)\n",
    "    for algo_name, config in algos.items():\n",
    "        gs =  GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)\n",
    "        gs.fit(X,y)\n",
    "        scores.append({\n",
    "            'model': algo_name,\n",
    "            'best_score': gs.best_score_,\n",
    "            'best_params': gs.best_params_\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(scores,columns=['model','best_score','best_params'])"
   ]
  },
  {
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   "execution_count": 28,
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    },
    "id": "ZzUQ79Zj60mi",
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       "               model  best_score  \\\n",
       "0  linear_regression    0.912102   \n",
       "1              lasso    0.912150   \n",
       "2      decision_tree    0.848094   \n",
       "3      random_forest    0.908751   \n",
       "\n",
       "                                                                             best_params  \n",
       "0                             {'copy_X': True, 'fit_intercept': True, 'normalize': True}  \n",
       "1                                                    {'alpha': 2, 'selection': 'cyclic'}  \n",
       "2                                               {'criterion': 'mse', 'splitter': 'best'}  \n",
       "3  {'max_depth': 31, 'max_features': 'auto', 'min_samples_leaf': 1, 'n_estimators': 106}  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', 100)\n",
    "find_best_model_using_gridsearchcv(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EWAMghG37Bdv"
   },
   "source": [
    "<h2 style=\"color:Peru;\"> Model Building </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "jIyodhexF540"
   },
   "outputs": [],
   "source": [
    "# Linear Regression\n",
    "lr = LinearRegression(copy_X=True, fit_intercept=True, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F8kQTj-LhFfI",
    "outputId": "7f246a5f-8865-4dd8-f409-0d06ccf70b42"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(normalize=True)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the model\n",
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4AGMvPLAhKv5",
    "outputId": "c85ff930-9626-4ff0-c952-4347ad32c881"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8971342853077842"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking score on test data\n",
    "lr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "Wbkip2frhNYG"
   },
   "outputs": [],
   "source": [
    "# predict on test data \n",
    "y_pred= lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qHGwHQHEhYRq"
   },
   "source": [
    "<h2 style=\"color:Peru;\"> Prediction </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "RfBOp32PhRD9"
   },
   "outputs": [],
   "source": [
    "def predict_profit(r_d_expenses,administration_expenses,marketing_expenses,state):    \n",
    "    '''\n",
    "    predict profit function takes four argument and \n",
    "    convert the data into the required format for prediction. \n",
    "    '''\n",
    "    \n",
    "    state_index = np.where(X.columns=='state_'+str(state))[0][0]\n",
    "\n",
    "    x = np.zeros(len(X.columns))\n",
    "    x[0] = r_d_expenses\n",
    "    x[1] = administration_expenses\n",
    "    x[2] = marketing_expenses\n",
    "    if state_index >= 0:\n",
    "        x[state_index] = 1\n",
    "\n",
    "    return lr.predict([x])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cCQQhMOLhjQD",
    "outputId": "b4717a56-f35d-4c10-f200-1ec0f19f9187"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95673.6755478764"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict on random values \n",
    "predict_profit(55067.95,102077.25,212117.91,'New York')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dSN9Q0Dph0jP"
   },
   "source": [
    "<h2 style=\"color:Peru;\"> Save Model </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "VAIWi3klhwLz"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "# save the model for future use \n",
    "pickle.dump(lr,open('startp_profit_prediction_lr_model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "3_TLqfiQh-3n"
   },
   "outputs": [],
   "source": [
    "#save the data columns\n",
    "import json\n",
    "\n",
    "columns = {'data_columns' : [col.lower() for col in X.columns]}\n",
    "\n",
    "with open(\"columns.json\",\"w\") as f:\n",
    "    f.write(json.dumps(columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bwpzuin5iEnD"
   },
   "outputs": [],
   "source": []
  }
 ],
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