ibnummuhammad
/

coal-price-forecast

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ibnummuhammad commited on Apr 7

Commit

dc9fbf8

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1 Parent(s): a663bf1

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Files changed (1) hide show

penguins_binary_classification.ipynb +328 -10

penguins_binary_classification.ipynb CHANGED Viewed

@@ -6,7 +6,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd"
    ]
   },
   {
@@ -396,7 +400,7 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x1573343e0>"
       ]
      },
      "execution_count": 5,
@@ -428,12 +432,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "# One-hot encode the categorical data and sort by flipper_length_mm\n",
     "df_dummy = pd.get_dummies(df, dtype=int).sort_values(\n",
     "    by=\"flipper_length_mm\", ascending=True\n",
@@ -876,6 +874,7 @@
    "outputs": [],
    "source": [
     "# Select the features and the target variable\n",
     "X = df_dummy[[\"flipper_length_mm\"]]\n",
     "Y = df_dummy[\"species_Gentoo\"]"
    ]
@@ -1482,6 +1481,325 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
     "X_gentoo = df_dummy[df_dummy[\"species_Gentoo\"] == 1][\"flipper_length_mm\"].values\n",
@@ -1490,7 +1808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1512,7 +1830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {

    "metadata": {},
    "outputs": [],
    "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
     {
      "data": {
       "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x166cf13d0>"
       ]
      },
      "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "# One-hot encode the categorical data and sort by flipper_length_mm\n",
     "df_dummy = pd.get_dummies(df, dtype=int).sort_values(\n",
     "    by=\"flipper_length_mm\", ascending=True\n",
    "outputs": [],
    "source": [
     "# Select the features and the target variable\n",
+    "# X = df_dummy[[\"bill_length_mm\", \"bill_depth_mm\", \"flipper_length_mm\", \"body_mass_g\"]]\n",
     "X = df_dummy[[\"flipper_length_mm\"]]\n",
     "Y = df_dummy[\"species_Gentoo\"]"
    ]
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y_predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [],
    "source": [
     "X_gentoo = df_dummy[df_dummy[\"species_Gentoo\"] == 1][\"flipper_length_mm\"].values\n",
   },
   {
    "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
   },
   {
    "cell_type": "code",
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {