From 57a42007dda02102c7caf4b3382ea20acef88e98 Mon Sep 17 00:00:00 2001 From: Paskal Date: Sat, 7 Jun 2025 14:39:03 +0545 Subject: [PATCH] comment --- 2-Regression/3-Linear/notebook.ipynb | 578 +-------------------------- 1 file changed, 18 insertions(+), 560 deletions(-) diff --git a/2-Regression/3-Linear/notebook.ipynb b/2-Regression/3-Linear/notebook.ipynb index 38e53549..68b55550 100644 --- a/2-Regression/3-Linear/notebook.ipynb +++ b/2-Regression/3-Linear/notebook.ipynb @@ -17,9 +17,6 @@ { "cell_type": "code", "execution_count": 2, - - "execution_count": 3, - "metadata": {}, "outputs": [ { @@ -217,11 +214,7 @@ "[5 rows x 26 columns]" ] }, - "execution_count": 2, - - "execution_count": 3, - "metadata": {}, "output_type": "execute_result" } @@ -234,28 +227,19 @@ "\n", "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n", "\n", - "pumpkins.head()\n", - "\n" + "pumpkins.head()\n" ] }, { "cell_type": "code", - "execution_count": 3, - - "execution_count": 4, - "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_7488\\2637987050.py:9: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - - "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_22516\\2637987050.py:9: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n" ] }, @@ -366,11 +350,7 @@ "74 15.0 13.636364 " ] }, - "execution_count": 3, - - "execution_count": 4, - "metadata": {}, "output_type": "execute_result" } @@ -411,27 +391,16 @@ }, { "cell_type": "code", - "execution_count": 4, - - "execution_count": 6, - "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" ] }, "execution_count": 4, - - "" - ] - }, - "execution_count": 6, - "metadata": {}, "output_type": "execute_result" }, @@ -453,27 +422,16 @@ }, { "cell_type": "code", - "execution_count": 5, - - "execution_count": 8, - "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" ] }, "execution_count": 5, - - "" - ] - }, - "execution_count": 8, - "metadata": {}, "output_type": "execute_result" }, @@ -495,7 +453,6 @@ }, { "cell_type": "code", - "execution_count": 6, "metadata": {}, "outputs": [ @@ -523,44 +480,17 @@ "text": [ "-0.14878293554077535\n", "-0.16673322492745407\n" - - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-0.14878293554077535\n", - "-0.16673322492745407\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_22516\\2521659294.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", - " day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n" - ] } ], "source": [ - - - "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n", - "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n", "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))" ] }, { "cell_type": "code", - "execution_count": 14, - - "execution_count": 11, - "metadata": {}, "outputs": [ { @@ -579,20 +509,12 @@ "colors = ['red', 'blue', 'green', 'yellow']\n", "for i, var in enumerate(new_pumpkins['Variety'].unique()):\n", " df = new_pumpkins[new_pumpkins['Variety'] == var]\n", - " ax = df.plot.scatter('DayOfYear', 'Price', ax=ax, c=colors[i], label=var)\n" - - " ax = df.plot.scatter('DayOfYear', 'Price', ax=ax, color=colors[i], label=var) " - ] }, { "cell_type": "code", - "execution_count": 15, - - "execution_count": 14, - "metadata": {}, "outputs": [ { @@ -601,11 +523,7 @@ "" ] }, - "execution_count": 15, - - "execution_count": 14, - "metadata": {}, "output_type": "execute_result" }, @@ -626,11 +544,7 @@ }, { "cell_type": "code", - "execution_count": 18, - - "execution_count": 21, - "metadata": {}, "outputs": [ { @@ -639,19 +553,13 @@ "" ] }, - - "execution_count": 21, - + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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25Z0qa5srMmihSFoz/8S0FZdzjf+5AoCP0M1UG3rxKVwZWabbb43zV0xbcTnX+J8rAPgIyUxtugP0r2jvRGS5bmt58U5/xLQVl3ON/7kCgM+QzJwpHddQ7f58f8S0FZdzjW9MAPAhq8nMunXrZNSoUdKuXTtJSUmRxYsXR+wvKSmRW265RTp06CCNGzeWnj17yuOPPy5WpXetYX+2P2Laisu5xjcmAPiQ1WTm0KFD0rdvX3nkkUcq3T9z5kxZtmyZPP300/LRRx/J9OnTTXLz8ssvizUZ3csGaKbUjyzXbS2Px0wUGzFtxeVc43+uAOAzVpOZkSNHyj333CNXXXVVpfvXr18vEyZMkCFDhsg555wjN9xwg0l+3n77bbFKZ5q0yYks020t91NMW3E51/ifKwD4SFJPzb744otNK8ykSZNMV9SaNWtkx44d8sADD1T5PaWlpeYRUlxcHPsD0ymzQ5eVDdDUcQ2JuDeIjZi24nKu8Y0JAD6T1MnM73//e9Mao2NmUlNTpV69evLEE0/I4MGDq/yeuXPnyt13352YA9SLTqIvPDZi2orLuQIAXJ/NpMnMxo0bTevM1q1b5f7775epU6fKypUV7ssRZs6cOVJUVFT+2Lt3b0KPGQAAJFbStsx88803cvvtt8uiRYvkiiuuMGV9+vSRbdu2yX333Sc5ORXGGfyfhg0bmgcAAAiGpG2ZOXbsmHlo11K4+vXry8mTJ60dFwAASC5WW2b0PjL5+aduDLZ7927T8tK8eXPp1KmTXHbZZTJr1ixzj5nOnTvL2rVrZcGCBfK73/3O5mEDAIAkkuJ5nmcruM5OGjp06GnlOh17/vz5UlhYaMbALF++XA4cOGASGh0QPGPGDHOTvWjobKbMzEwzfiYjIyMOZwEAAGLtTK7fVpOZRCCZAQDA39fvpB0zAwAAEA2SGQAA4DSSGQAA4DSSGQAA4DSSGQAA4DSSGQAA4DSSGQAA4DSSGQAA4DSSGQAA4DSSGQAA4DSSGQAA4DSrq2Y7r3iHSMkukfRskYxuto/Gf2zVr424vJcAoNZIZmqj9IDI+vEi+/JOlbXNFRm0UCStmc0j8wdb9WsjLu8lAKgzuplqQy8+hSsjy3T7rXG2jshfbNWvjbi8lwCgzkhmatMdoH9Feyciy3Vby4t32joyf7BVvzbi8l4CgJggmTlTOq6h2v35iToSf7JVvzbi8l4CgJggmTlT6V1r2J+dqCPxJ1v1ayMu7yUAiAmSmTOV0b1sgGZK/chy3dZyZqK4Wb824vJeAoCYIJmpDZ1p0iYnsky3tRzu1q+NuLyXAKDOUjzP88THiouLJTMzU4qKiiQjIyPGL76zbFwD9waJD1v1ayMu76VTuL+QP+NyLyXE8fpNMgMgOXB/Ic4VqOX1m24mAMmB+wtxrkAtkcwAsI/7C3GuQB2QzACwj/sLxTemrbjcSwkJQjIDwD7uLxTfmLbici8lJAjJDAD7uL8Q5wrUAckMgOTA/YU4V6CW6jQ1++jRo7J7927p2rWrpKamSjJiajbgGO4v5M+43EsJyXafmcOHD8u0adPkqaeeMts7duyQb33rW6asffv2Mnv2bEkWJDMAALgn7veZmTNnjvzP//yPrFmzRho1alRenpOTI3/9619r85IAAAC1Uqu+ocWLF5uk5aKLLpKUlJTy8l69esmuXTVMxQMAAIihWrXMfPnll9KqVavTyg8dOhSR3AAAACRlMvPtb39bXn311fLtUALz5z//WQYOHBi7owMAAIhHN9Ovf/1rGTlypHz44Ydy/Phxeeihh8z/169fL2vXrq3NSwIAACSuZeaSSy6Rbdu2mUSmd+/esnz5ctPttGHDBunXr1/tjgQAACDR95lxAVOzAQBwT9ynZr/22muSl5d3WrmWLV26tDYvCQAAUCu1Smb0pngnTlRY0l1Xdfe8pLphHgAA8L9aJTM7d+6Unj17nlZ+7rnnSn4+S7oDAIAkT2a0D+vjjz8+rVwTmSZNmsTiuFCV4h0iBUvL1jnxe9z8/yey/lqR/PmSUAV5Iu/9QqRgReLqoqbXDNLPHQASMTX7yiuvlOnTp8uiRYvMIpOhROa2226TH/zgB7V5SdSk9IDI+vEi+8LGKrXNLVt5Nq2Zv+Lu3yqyfKCId6xs+5OnRTbfIJL7tkjzCyRuDu4SWT5ApHT/qbKGWSK5m0XSu8SnLmp6zSD93AEgkbOZdGTxiBEjZMuWLdKhQwdT9tlnn8mll14qL774ojRt2lSShW9mM60eIVK4UsQLG6uUUl+kTY7I0GX+irsw7VQiEy6lgci4oxI3L7SITGTCE5qxX8WnLmp6zSD93AGgltfvWrXM6IvrDfJWrFhhFpxs3Lix9OnTRwYPHlybl0M0Tf3hfyGH6IVGy7ULIKObP+Jq11JliYyJe6ysyyl7osSla6myREZpef682NdFTfVbsDw4P3cAqINaJTOhJQyGDx9uHoizkhoW7yzJj8/FxUbcL9bUsP/1+CQz+zfVEHdV7OuipvrdvzH2MZP5/QYA8U5mHn74YbnhhhukUaNG5v/VufXWW2t7PKhMetca9mf7J26rIWVjZKrcf7nERdaA6ve3Glb9cdWmLmqq36yLgvNzB4BEjJnp0qWLGSOTlZVl/l/lC6akVDrTyRbGzDgYlzEzjJkBEHjFZ3D9ZjkDVxz9h8hb4xI/u8RG3APbRPK+E5nQaCIT79lMJbtF8vpXPZspHnVR02sG6ecePmZHu7q0BSiR3Vm24gJIfDJz7Ngxc3O8JUuWyHnnnSfJzjfJTIgOvtQxCwn/RW8hrg721TEy2rUUj3EyVdH7y+zfIJI1UKTddxNTFzW9ZqLr38bUbKahA0hky0z79u1l5cqVJDOAX9noZqJLDUAiF5qcOnWq/OY3v5Hjx4/X5tsBJLPQ1Ozwi3vFqdl+iGkzLoCYqlUys3nzZnNzvE6dOklubq6MGTMm4hGtdevWyahRo6Rdu3Zm4PDixYtPe85HH31k7iqs2ZkuldC/f3/Zs2dPbQ4bQKymZvshps24AOzfZ0bv8Dt27Ng6Bz906JD07dtXJk2aVGkStGvXLrnkkktk8uTJcvfdd5tmpg8++MBMDwcg/pmaHaTbDwCwm8ycPHlS/vM//1N27NghR48elWHDhsldd91l7gBcGyNHjjSPqvzbv/2bfO9735Pf/va35WWhtaAAa7NabMx60Tsj6w0FEzEYOqN72QDYqsaRxOOcbcS0GReAvW6mX/3qV3L77bdLenq6GQSsN8/T8TPxoInTq6++Kt27dzddWa1atZIBAwZU2hUVrrS01AwaCn8AUc9q0cGgS3qIrPmeyJLuZds6TTma/fFadFPvu/P2T8pu2vf29WXbOn09ns77uYh3MrJMt3ve7q+YSmctaeISTre1HID/kpkFCxbIo48+Knl5eSapeOWVV+S///u/TeIRa1988YWUlJTIvffeaxa1XL58uVx11VWmO2rt2rVVft/cuXPN+JrQo2PHjjE/NviUTs/Vv9DD6bbebyWa/fEQvnp4iG7rfXjiaXWuBqpQ6ImsyvFXTKXTr3XW0vd3iAx5reyrbjMtG/BnMqMDb7XbJyQnJ8cM3C0oKIj5gYUSpCuvvFJmzJghF1xwgcyePVu+//3vy+OPP17l982ZM8dM4wo99u7dG/Njgw/VNKsltOhjIme9RLPoZjzYiGvrXMNpl1K7kXQtAX5PZnQqdsXBtw0aNDA30ou1Fi1aSGpqqvTs2TOiXO9tU91spoYNG5qBwuEPICGLPsZaNItuxoONuLbOFUDwBgDr/fUmTpxoEoaQI0eOyJQpU8y06RCdtl1XaWlpZhr29u3bI8p18HHnzp3r/PpA0i/6aGvRTRtxbZ0rgOAlMxMmTDit7Mc//nGtg+uYmPz8U3/R7t69W7Zt2ybNmzc397CZNWuWXHPNNTJ48GAZOnSoLFu2zIzTWbOmhr/igFjPamk3PPGzXrIni2y+qepFN+M1q8lGXFvnCsAXrC40qUmJJimVJU3z55f1kT/55JNmUO9nn30mPXr0MPeb0XE00WI5Azi96KOtRTdtxLV1rgCSEqtmhyGZgfOLPtpcdNNGXFvnCiCpkMyEIZkBAMA9cV9oEgAAIFmQzAAAAKeRzAAAAKeRzAAAAKeRzAC1ufX++msTc4v9kII8kfd+IVKwInExbcW1da5bZoi82ldk60/9HVO99yuRlcNE3r/X358bW3H5vCYcs5mAM1nBuuLCj/G+D8rBXSLLB4iU7j9V1jBLJHezSHqX+MS0FdfWue5ZLPLmVaeXD14i0uEK/8RU+1aJrNaFO8N/7aeIXL5GpPVg/3xubMXl8yqxjMnU7DAkM4iZhWlV36F23NH4xHyhReQvi/BfGmO/ik9MW3FtnetfUqreN97zT0wTVxvjK3v9FJHxZYv7+uJzYysun1eJZUymZgN+WNVZm28r+2WhtDxezbo24to6V+3mqU48un9sxAx1LVWayCgvPl1OQVr5nc9r/M+1GiQzQLKu6rx/Uw37N8Q+pq24ts7181XV7y9c4Y+YJm4N79HC5bGPGaSV3/m8xv9cq0EyA0S7qnO1++OwqnPWgBr2D4x9TFtxbZ1r62HV72/zXX/ENHFreI+2Ge6Pz42tuHxe43+u1WDMDBAt+uCd74OvFGNmGDMTK3xehTEzQLLTWRD6izBcaHZE3GJuLvvlEC40YyCebMS1da46g+hMyl2NqXTWkiYuEf5vNpOfPje24vJ5lbifaxVomQFcWNVZB9RpP7Q237aLUzdEssS1da468FbHq2g3T7/7/BtT6WBfHSOjXUvnz05MzCCt/M7nNSaYmh2GZAYAAPfQzQQAAAKDZAYAADiNZAYAADiNZAYAADgt1fYBAEBgFe8QKdklkp4tktHN9tEAziKZAYBEKz0gsn68yL68U2Vtc0UGLRRJa2bzyAAn0c0EAImmiUzhysgy3X5rnK0jApxGMgMAie5a0hYZ70RkuW5refFOW0cGOItkBgASScfIVLs/P1FHAvgGyQwAJFJ61xr2ZyfqSADfIJkBgETK6F422DelfmS5bms5s5qAM0YyAwCJprOW2uRElum2lgM4Y0zNBoBE0+nXQ5eVDfbVMTLcZwaoE5IZALBFExiSGKDO6GYCAABOI5kBAABOI5kBAABOI5kBAABOYwAwUFFBnsj+TSJZA0Xafde/MQHAJ0hmgJCDu0SWDxAp3X+qrGGWSO5mkfQu/okJAD5DNxMQUjGpULqd199fMQHAZ0hmgFA3T8WkIkTLC1b4IyYA+BDJDKB0vEq1+zf4IyYA+BDJDKCyBtSwf6A/YgKAD5HMAKpdbtnA28poeTxmGNmICQA+RDIDhOgMoorJRWhmkZ9iAoDPMDUbCNGp0GO/Kht4q+NVEnHPFxsxAcBnUjzP88THiouLJTMzU4qKiiQjI8P24QAAgBhfv+lmAgAATiOZAQAATiOZAQAATiOZAQAATmM2E3CmgrSqNufqv5i24nKu/oxbvEOkZJdIerZIRjexhWQGiFaQVtXmXDlXl2Paihukcy09ILJ+vMi+vFNlbXNFBi0USWsmicbUbCBaL7SofGFI/aWh94rxS0xbcTlXztX1uEE619UjRApXingnTpWl1BdpkyMydFmwpmavW7dORo0aJe3atZOUlBRZvHhxlc+dMmWKec6DDz6Y0GMEAreqNuca37ica3xj2oobpHMt3lHWIhOeyCjd1vLinZJoVpOZQ4cOSd++feWRRx6p9nmLFi2SjRs3mqQHsCJIq2pzrvGNy7nGN6atuEE615JdNezPl0CNmRk5cqR5VOfvf/+7TJs2TfLy8uSKK66o8TVLS0vNI7yZCqizIK2qzbnGNy7nGt+YtuIG6VzTu9awP1sSLamnZp88eVKuvfZamTVrlvTq1Suq75k7d67pYws9OnbsGPfjRAAEaVVtzjW+cTnX+Ma0FTdI55rRvWywr46RCafbWm5hVlNSJzO/+c1vJDU1VW699daov2fOnDlmsFDosXfv3rgeIwIkSKtqc66cq8sxbcUN0rkOWlg22Decbmu5BUkzm0kH9+rYmNGjR5vtrVu3mm6ld955p3yszDnnnCPTp083j2gxmwkxZ2OFa1uranOu/otpKy7n6s+4xTvLxsjE4T4zZ3L9TtpkRmctzZw5U+rVO9V4dOLECbOtXUeffPJJVK9LMgMAgHvO5PqdtDfN07EyOTmRTVi5ubmm/Prrr7d2XAAAILlYTWZKSkokP//UFK7du3fLtm3bpHnz5tKpUyfJyorsA2zQoIG0adNGevToYeFoAQBAMrKazGzZskWGDh1avq3dSmrChAkyf/58i0cGAABcYTWZGTJkiJzJkJ1ox8kAzi2sliSLtSXFcdmqiyCdK+AzSTtmBkg68VhYLckWa7N6XLbqIkjnCvhUUt9nBkgqevHRhdXC6fZb45LrNWPBxnHZqosgnSvgUyQzgK2F1ZJwsTZrx2WrLoJ0roCPkcwAthZWS8LF2qwdl626CNK5Aj5GMgPYWlgtCRdrs3ZctuoiSOcK+BjJDGBrYbUkXKzN2nHZqosgnSvgYyQzgM2F1ZJssTarx2WrLoJ0ruHjdgqWJnZ8jo2YQTvXAEuatZnihbWZ4MTCanFcrM2547JVF0E41yBNQw/SufqUkwtNxgvJDAD8n9UjyqaAh8+k0u4tbRUausw/MYN2rj51JtdvupkAIAiCNA09SOcKg2QGAIIgSNPQg3SuMEhmACAIgjQNPUjnCoNkBgCCIEjT0IN0rjBIZgAgKII0DT1I5wpmMwFA4ARhGrrNuMl6qwXHMDU7DMkMAADuYWo2AAAIDJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNJIZAADgNKvJzLp162TUqFHSrl07SUlJkcWLF5fvO3bsmPz85z+X3r17S5MmTcxzrrvuOikoKLB5yAAAIMlYTWYOHTokffv2lUceeeS0fYcPH5Z33nlH7rjjDvP1xRdflO3bt8sPfvADK8cKAACSU4rneZ4kAW2ZWbRokYwePbrK52zevFm+853vyKeffiqdOnWK6nWLi4slMzNTioqKJCMjI4ZHDAAA4uVMrt+p4hA9IU16mjZtWuVzSktLzSO8MgAAgH85MwD4yJEjZgzNuHHjqs3Q5s6dazK50KNjx44JPU4AAJBYTiQzOhj4hz/8oWiP2GOPPVbtc+fMmWNacEKPvXv3Juw4AcRA8Q6RgqUixTttHwkAR6S6ksjoOJlVq1bV2G/WsGFD8wDgmNIDIuvHi+zLO1XWNldk0EKRtGY2jwxAkqvnQiKzc+dOWblypWRlZdk+JADxoolM4crIMt1+a5ytIwLgCKstMyUlJZKfn1++vXv3btm2bZs0b95c2rZtK1dffbWZlr1kyRI5ceKEFBYWmufp/rS0NItHDiDmXUvhLTIh3omycu1yyuhm48gAOMBqMrNlyxYZOnRo+fbMmTPN1wkTJshdd90lL7/8stm+4IILIr5v9erVMmTIkAQfLYC4KdlVw/58khkAyZnMaEJS3W1ukuQWOADiLb1rDfuzE3UkAByU1GNmAARERveywb4p9SPLdVvLaZUBUA2SGQDJQWcttcmJLNNtLQcAl6dmAwgInX49dFnZYF8dI6NdS7TIAIgCyQyA5KIJDEkMgDNANxMAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHAayQwAAHBaqu0DAICkUJAnsn+TSNZAkXbf9W9MVbxDpGSXSHq2SEa3xMUF4oRkBkCwHdwlsnyASOn+U2UNs0RyN4ukd/FPTFV6QGT9eJF9eafK2uaKDFooktYsfnGBOKObCUCwVUwqlG7n9fdXTKWJTOHKyDLdfmtcfOMCcUYyAyC4tJunYlIRouUFK/wRM9S1pC0y3onIct3W8uKd8YkLJADJDIDg0vEq1e7f4I+YSsfIVLs/Pz5xgQQgmQEQXFkDatg/0B8xVXrXGvZnxycukAAkMwCCq11u2cDbymh5PGYY2YipMrqXDfZNqR9ZrttazqwmOIxkBkCw6QyiislFaGaRn2IqnbXUJieyTLe1HHBYiud5nvhYcXGxZGZmSlFRkWRkZNg+HADJSgfe6niVhN5nxkJMpYN9dYwM95mBT67fJDMAAMDp6zfdTAAAwGkkMwAAwGkkMwAAwGkkMwAAwGkkMwAAwGkkMwAAwGkkMwAAwGkkMwAAwGkkMwAAwGkkMwAAwGmptg8g3kKrNehtkQEAgBtC1+1oVl3yfTJz8OBB87Vjx462DwUAANTiOq5rNAV6ocmTJ09KQUGBnH322ZKSkmI9y9Skau/evSx6GQPUZ+xRp7FFfcYW9RmsOvU8zyQy7dq1k3r16gW7ZUYroEOHDpJM9A2TbG8al1GfsUedxhb1GVvUZ3DqNLOGFpkQBgADAACnkcwAAACnkcwkUMOGDeU//uM/zFfUHfUZe9RpbFGfsUV9xl5Dn9Sp7wcAAwAAf6NlBgAAOI1kBgAAOI1kBgAAOI1kBgAAOI1kpo7mzp0r/fv3N3cYbtWqlYwePVq2b99e6XN1rPXIkSPNnYgXL14csW/Pnj1yxRVXyFlnnWVeZ9asWXL8+HEJmmjrc8OGDTJs2DBp0qSJudHT4MGD5Ztvvinff+DAAfnRj35k9jVt2lQmT54sJSUlEkTR1GlhYaFce+210qZNG1On//RP/yQvvPBCxHOo0zKPPfaY9OnTp/wmYwMHDpSlS5eW7z9y5IhMnTpVsrKyJD09XcaOHSuff/55xGvweY+uPvU9N23aNOnRo4c0btxYOnXqJLfeeqsUFRVFvAb1eWbvUT9ek0hm6mjt2rXmF9fGjRtlxYoVcuzYMRk+fLgcOnTotOc++OCDlS6pcOLECfOmOXr0qKxfv16eeuopmT9/vtx5550SNNHUpyYyI0aMMOVvv/22bN68WW655ZaI213rRfeDDz4wr7FkyRJZt26d3HDDDRJE0dTpddddZxKcl19+Wd577z0ZM2aM/PCHP5S//e1v5c+hTsvoHcXvvfde2bp1q2zZssUk1VdeeaWpGzVjxgx55ZVX5LnnnjN1r8upaH2G8HmPvj617vRx3333yfvvv2/qadmyZSaRDqE+T1fTe9SX1ySdmo3Y+eKLL3Squ7d27dqI8r/97W9e+/btvX379pn9ixYtKt/32muvefXq1fMKCwvLyx577DEvIyPDKy0t9YKssvocMGCA9+///u9Vfs+HH35ovmfz5s3lZUuXLvVSUlK8v//9717QVVanTZo08RYsWBDxvObNm3tPPPGE+T91Wr1mzZp5f/7zn72vv/7aa9Cggffcc8+V7/voo49M3W3YsMFs83mPvj4r8+yzz3ppaWnesWPHzDb1GZ2Kdeq3axItMzEWav5s3rx5ednhw4dl/Pjx8sgjj5hm/Iq0paF3797SunXr8rLc3FyzAFjFTDro9fnFF1/Ipk2bTLPnxRdfbOrssssukzfffDOiPrUb5Nvf/nZ5WU5Ojmm50e8Nusreo1qXf/3rX02zvi7O+swzz5jukiFDhpj91Gnl9C9YrStt5dKmfP1LWFu+tG5Czj33XNM9onWo+LxHX59VvX+16yQ1tWxpQeqzepXVqR+vSb5faDKR9CIwffp0GTRokJx//vnl5drsrBcLbearjI5XCH/TqNC27guqyurz448/Nl/vuusu0/R8wQUXyIIFC+Tyyy83zdDdunUzdabJTjj9xacX7yDXZ3Xv0WeffVauueYaM85D60r7yRctWiTZ2dlmP3UaSbvi9MKgCZ+Oi9G66tmzp2zbtk3S0tJM4lfx8xyqJz7v0ddnRV999ZX88pe/jOjepD4rV12d+vGaRDITQzouQS+o4a0EOgZh1apVEWMPUPv61IuxuvHGG+X66683/7/wwgvl9ddflyeffNIMdsWZ1am644475Ouvv5aVK1dKixYtzGBAHTPzxhtvmL/QEEkHpGrioq0Ezz//vEyYMMGMj0Fs6zM8odFWAR3HoWX6xwxqV6f5+fm+vCaRzMSIDkANDYrUwVch+qbZtWvXaX+p6QyHSy+9VNasWWOa+XQga7jQ7IfKmgCDXJ9t27Y1Xyv+1XbeeeeZ0fehOtPuqHA6Cl+7UIJan9XVqb4///CHP5gkp1evXqasb9++JpHRZujHH3+cOq1AW19CrVb9+vUzg9Afeugh07qlgyY1MQz/zOvnOVRPfN6jr88//vGPpuzgwYNm0L/OyNMWhgYNGpR/L/VZuarqVGeF+fGaxJiZOtKpbXqR0A+YJi5dunSJ2D979mx59913TYYceqgHHnhA5s2bZ/6vTYHaJBh+sdAZI9ovXFlTa5Dr85xzzpF27dqdNrV4x44d0rlz5/L61IuJjl8I0dfSVp0BAwZI0NRUp9p/rsJng6n69euXt4RRp9XTeigtLTUXDb3QakthiL5XNdEOjVfg8x59fYZaZHT2nV6ctaW7UaNGEc+lPqMTqlPfXpNsj0B23U033eRlZmZ6a9asMaPCQ4/Dhw9X+T0VR44fP37cO//8873hw4d727Zt85YtW+a1bNnSmzNnjhc00dTnAw88YEbV64yRnTt3mplNjRo18vLz88ufM2LECO/CCy/0Nm3a5L355ptet27dvHHjxnlBVFOdHj161MvOzvYuvfRSU19aj/fdd5+ZqfTqq6+Wvw51Wmb27NlmJtju3bu9d99912xrXS1fvtzsnzJlitepUydv1apV3pYtW7yBAweaRwif9+jrs6ioyMxe7N27t3lfhr9/tR4V9Xm6mt6jfrwmkczUkb4JKnvMmzev2u8Jf+OoTz75xBs5cqTXuHFjr0WLFt5tt91WPvUwSKKtz7lz53odOnTwzjrrLHOheOONNyL279+/31xo09PTTeJz/fXXewcPHvSCKJo63bFjhzdmzBivVatWpk779Olz2lRt6rTMpEmTvM6dO5vpwfoL/vLLL4+4SHzzzTfezTffbKbCal1eddVV5uIbjs97dPW5evXqKt+/eqEOoT7P7D3qx2tSiv5ju3UIAACgthgzAwAAnEYyAwAAnEYyAwAAnEYyAwAAnEYyAwAAnEYyAwAAnEYyAwAAnEYyAwAAnEYyA8A33nrrLbPKt66PNHr0aNuHAyBBSGYAnLGJEydKSkqKeWji0Lp1a/nud78rTz75ZPnilLGkq31fdtllZtXks846S/r37y/z588/7XkzZ86UCy64QHbv3i0zZswwx/bmm29GPOfQoUPyrW99S37605/G/DgB2EEyA6BWRowYIfv27ZNPPvlEli5dKkOHDpV//dd/le9///ty/PjxmMX5/e9/L1deeaUMGjRINm3aZFb8/ed//meZMmXKaQnJrl27ZNiwYdKhQweT/EybNs0kXprAhPzsZz+Txo0byz333COxdvTo0Zi/JoAo2F4cCoB7JkyY4F155ZWnlb/++utm0bonnnjCbN9///1m9V1dcFEXBtUVvEOLU5aUlHhnn322Wf08nC54p88vLi729uzZ4zVo0MCbOXPmabEefvhhE2vjxo1m0cHKFtLURR/PO+88b+rUqeZ7dCVrXXxPV7M+ceKE9+tf/9o755xzzKrrurhm+LHoysG6YF9of/fu3b0HH3yw0nq45557vLZt25rnAkg8WmYAxIy2ivTt21defPFFs12vXj15+OGH5YMPPpCnnnpKVq1aZVpGVJMmTUwLy7x58yJeQ7evvvpq06X0/PPPy7FjxyrtErrxxhslPT1dFi5cKB07djStRBkZGfLggw+a/19zzTXSqFEjWbBggfzpT3+Sl156SSZNmiS333679OvXT+bOnWv2Pf744+b4tFvqxz/+saxdu9a8vnaXaQvPc889Jx9++KHceeed5nufffbZiON4/fXXZfv27bJixQrTHQbAAgsJFACftsyoa665xrSGVEZbPrKyssq3N23a5NWvX98rKCgw259//rmXmprqrVmzxmxPmTLFy8zMrPI4tDVl5MiR5dv6XG2RqejOO+/06tWr5/Xr1887duyYd+TIEdP6s379+ojnTZ482Rs3blyV8bSFZ+zYsRH10Lp1a6+0tLTK7wEQf7TMAIgpz/PMwGC1cuVKufzyy6V9+/ampeXaa6+V/fv3y+HDh83+73znO9KrVy/TaqOefvpp6dy5swwePDimx3THHXeYlpbZs2dLamqq5Ofnm2PQQcvauhN6aEuNjrsJeeSRR0wrTsuWLc1+beHZs2dPxGvr7Km0tLSYHi+AM0MyAyCmPvroI+nSpYsZGKyDgfv06SMvvPCCbN261SQHFQfK/uQnPymfmaRdTNdff315MtS9e3cpKiqSgoKC0+Loa2jioc+piSYw4V9LSkrM11dffVW2bdtW/tDuJO3aUs8884zp3po8ebIsX77c7NdjqzjIV7vLANhFMgMgZnRMzHvvvSdjx441yYu2htx///1y0UUXmaSjsqREx6l8+umnZmyNJhMTJkwo36evo9Or9TUq0rEuOktp3LhxZ3ycPXv2lIYNG5pWluzs7IiHjr8J3bPm4osvlptvvlkuvPBCsy+81QZA8ij7MwUAzlBpaakUFhbKiRMn5PPPP5dly5aZQbXaGnPdddfJ+++/bwbv6tTqUaNGmeRAE5CKmjVrJmPGjJFZs2bJ8OHDzaDbkE6dOslvf/tbue2228xgXu2m0uRGB/PqYFwtHzBgwBkfu3Z5aauLDvrVhOuSSy4xLUB6jDqIWBOqbt26mW6nvLw809L0X//1X7J582bzfwBJJgHjcgD4jA58DU2B1gG7LVu29HJycrwnn3zSTHkO+d3vfmemLDdu3NjLzc31FixYYL7nH//4R6VTup999tlK47300kvepZde6jVp0sRMk9aBvBqroqoGACt9fZ32HXLy5Ekz1bpHjx5m+reegx7j2rVrzX4dJDxx4kTzmk2bNjXTymfPnu317ds3qoHQABInRf+xnVABCDZt9dBWEu2GYjAtgDNFNxMAa3RGkd4T5t577zX3jSGRAVAbDAAGYI2Ohzn33HOlTZs2MmfOHNuHA8BRdDMBAACn0TIDAACcRjIDAACcRjIDAACcRjIDAACcRjIDAACcRjIDAACcRjIDAACcRjIDAADEZf8f7v8zNPHTvz8AAAAASUVORK5CYII=", - "text/plain": [ "
" ] @@ -662,7 +570,6 @@ ], "source": [ "pie_pumpkins = new_pumpkins[new_pumpkins['Variety'] == 'PIE TYPE']\n", - "pie_pumpkins.plot.scatter('DayOfYear', 'Price', c='red', label='PIE TYPE')" ] }, @@ -684,18 +591,11 @@ "pie_pumpkins = new_pumpkins[new_pumpkins['Variety'] == 'PIE TYPE']\n", "correlation = pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price'])\n", "print('Correlation between Day of Year and Price for PIE TYPE:', correlation)" - - "pie_pumpkins.plot.scatter('DayOfYear', 'Price', color='orange', label='PIE TYPE')\n" - ] }, { "cell_type": "code", - "execution_count": 20, - - "execution_count": 22, - "metadata": {}, "outputs": [ { @@ -723,11 +623,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_7488\\3144308612.py:1: SettingWithCopyWarning: \n", - - "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_22516\\3144308612.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -742,7 +638,6 @@ }, { "cell_type": "code", - "execution_count": 23, "metadata": {}, "outputs": [ @@ -840,26 +735,22 @@ }, { "cell_type": "code", - "execution_count": 32, - "execution_count": null, - "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -869,7 +760,7 @@ } ], "source": [ - "plt.scatter(X_test, pred, color='red', label='Predicted Prices')" + "plt.scatter(X_test, pred, color='red', label='Predicted Prices')git config --global --add safe.directory D:/AI/MachineLearning/ML-For-Beginners" ] }, { @@ -881,14 +772,18 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Score on test set: 0.12719946902474621\n" + "ename": "NameError", + "evalue": "name 'LinearRegression' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpreprocessing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m PolynomialFeatures\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpipeline\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m make_pipeline\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m pipeline = make_pipeline(PolynomialFeatures(\u001b[32m2\u001b[39m), \u001b[43mLinearRegression\u001b[49m())\n\u001b[32m 6\u001b[39m pipeline.fit(X_train,y_train)\n\u001b[32m 7\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mScore on test set:\u001b[39m\u001b[33m\"\u001b[39m, pipeline.score(X_test, y_test))\n", + "\u001b[31mNameError\u001b[39m: name 'LinearRegression' is not defined" ] } ], @@ -900,446 +795,9 @@ "\n", "pipeline.fit(X_train,y_train)\n", "print(\"Score on test set:\", pipeline.score(X_test, y_test))\n", - "pred = pipeline.predict(X_test)" - - "text/html": [ - "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "LinearRegression()" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.model_selection import train_test_split\n", - "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1, 1)\n", - "y = pie_pumpkins['Price']\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n", - "lin_reg = LinearRegression()\n", - "lin_reg.fit(X_train, y_train)\n", - "#Remaining\n" - + "pred = pipeline.predict(X_test)\n", + "\n", + "#Lets start the fork" ] } ],