From 5c9504b03da0a334825d9e061e47894eaacb8358 Mon Sep 17 00:00:00 2001 From: Giada Sartori Date: Mon, 13 Jun 2022 14:40:47 +0100 Subject: [PATCH] done --- your-code/main.ipynb | 1671 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 1633 insertions(+), 38 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 9a2d1ae..e97d9ae 100755 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -12,17 +12,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# Import your libraries:\n", "\n", - "%matplotlib inline\n", - "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd" + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import StandardScaler" ] }, { @@ -38,12 +38,206 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Channel Region Fresh Milk Grocery \\\n", + "count 440.000000 440.000000 440.000000 440.000000 440.000000 \n", + "mean 1.322727 2.543182 12000.297727 5796.265909 7951.277273 \n", + "std 0.468052 0.774272 12647.328865 7380.377175 9503.162829 \n", + "min 1.000000 1.000000 3.000000 55.000000 3.000000 \n", + "25% 1.000000 2.000000 3127.750000 1533.000000 2153.000000 \n", + "50% 1.000000 3.000000 8504.000000 3627.000000 4755.500000 \n", + "75% 2.000000 3.000000 16933.750000 7190.250000 10655.750000 \n", + "max 2.000000 3.000000 112151.000000 73498.000000 92780.000000 \n", + "\n", + " Frozen Detergents_Paper Delicassen \n", + "count 440.000000 440.000000 440.000000 \n", + "mean 3071.931818 2881.493182 1524.870455 \n", + "std 4854.673333 4767.854448 2820.105937 \n", + "min 25.000000 3.000000 3.000000 \n", + "25% 742.250000 256.750000 408.250000 \n", + "50% 1526.000000 816.500000 965.500000 \n", + "75% 3554.250000 3922.000000 1820.250000 \n", + "max 60869.000000 40827.000000 47943.000000 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your observations here" + "customers.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Channel 0\n", + "Region 0\n", + "Fresh 0\n", + "Milk 0\n", + "Grocery 0\n", + "Frozen 0\n", + "Detergents_Paper 0\n", + "Delicassen 0\n", + "dtype: int64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#2. Check if there is any missing value in the dataset\n", + "\n", + "customers.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Channel 2\n", + "Region 3\n", + "Fresh 433\n", + "Milk 421\n", + "Grocery 430\n", + "Frozen 426\n", + "Detergents_Paper 417\n", + "Delicassen 403\n", + "dtype: int64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#3. Check and count the unique values of each column of the dataset\n", + "\n", + "customers.value_counts()\n", + "customers.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 1])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers['Channel'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 1, 2])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers['Region'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Frozen0.3458810.123994-0.0401931.000000-0.1315250.3909470.202046-0.202046-0.0068000.069421-0.041920
Detergents_Paper-0.1019530.6618160.924641-0.1315251.0000000.069291-0.6360260.636026-0.0222790.058526-0.021366
Delicassen0.2446900.4063680.2054970.3909470.0692911.000000-0.0560110.056011-0.027791-0.0448310.054252
Channel_10.169172-0.460720-0.6087920.202046-0.636026-0.0560111.000000-1.0000000.087638-0.060306-0.032613
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" + ], + "text/plain": [ + " Fresh Milk Grocery Frozen Detergents_Paper \\\n", + "Fresh 1.000000 0.100510 -0.011854 0.345881 -0.101953 \n", + "Milk 0.100510 1.000000 0.728335 0.123994 0.661816 \n", + "Grocery -0.011854 0.728335 1.000000 -0.040193 0.924641 \n", + "Frozen 0.345881 0.123994 -0.040193 1.000000 -0.131525 \n", + "Detergents_Paper -0.101953 0.661816 0.924641 -0.131525 1.000000 \n", + "Delicassen 0.244690 0.406368 0.205497 0.390947 0.069291 \n", + "Channel_1 0.169172 -0.460720 -0.608792 0.202046 -0.636026 \n", + "Channel_2 -0.169172 0.460720 0.608792 -0.202046 0.636026 \n", + "Region_1 -0.032760 -0.019358 -0.026598 -0.006800 -0.022279 \n", + "Region_2 -0.057832 -0.033217 0.046170 0.069421 0.058526 \n", + "Region_3 0.067375 0.039156 -0.009235 -0.041920 -0.021366 \n", + "\n", + " Delicassen Channel_1 Channel_2 Region_1 Region_2 \\\n", + "Fresh 0.244690 0.169172 -0.169172 -0.032760 -0.057832 \n", + "Milk 0.406368 -0.460720 0.460720 -0.019358 -0.033217 \n", + "Grocery 0.205497 -0.608792 0.608792 -0.026598 0.046170 \n", + "Frozen 0.390947 0.202046 -0.202046 -0.006800 0.069421 \n", + "Detergents_Paper 0.069291 -0.636026 0.636026 -0.022279 0.058526 \n", + "Delicassen 1.000000 -0.056011 0.056011 -0.027791 -0.044831 \n", + "Channel_1 -0.056011 1.000000 -1.000000 0.087638 -0.060306 \n", + "Channel_2 0.056011 -1.000000 1.000000 -0.087638 0.060306 \n", + "Region_1 -0.027791 0.087638 -0.087638 1.000000 -0.159274 \n", + "Region_2 -0.044831 -0.060306 0.060306 -0.159274 1.000000 \n", + "Region_3 0.054252 -0.032613 0.032613 -0.735233 -0.552059 \n", + "\n", + " Region_3 \n", + "Fresh 0.067375 \n", + "Milk 0.039156 \n", + "Grocery -0.009235 \n", + "Frozen -0.041920 \n", + "Detergents_Paper -0.021366 \n", + "Delicassen 0.054252 \n", + "Channel_1 -0.032613 \n", + "Channel_2 0.032613 \n", + "Region_1 -0.735233 \n", + "Region_2 -0.552059 \n", + "Region_3 1.000000 " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#4. Check the correlation among the different columns\n", + "\n", + "customers.corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Some visualization with a boxplot and correlation matrix\n", + "- Boxplot to show if there are any outliers\n", + "- Correlation plot to show the cardinality among the different columns\n", + "- Verify if there are some outliers, by checking if there is any value is lower than the Q1 and Q3" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Wlpa0IeadnZ00NjbS09Mz5/1nuo5CZxSSM1MnAIzahIATwjq9F8kkzOllVCgkJ9544w02b97MpZdeSkFBAZdeeimbN2/mjTfeOPbGORSX2WNBBe1kE+r0MjPNPx7XRfejiKby8nI/44wzvKOjw4eGhryjo8PPOOMMLy8vDzu1NHG5z0NbW5tXVlam/T4rKysjee8MyY75/jcnw/0oQv9iz/aiQhFN5eXlvnLlyrQ/8pUrV0auUCQSCR8aGkqLDQ0NeSKRCCmj6cWloEl2zedNtTIVCnVmS04kk0muvvpqdu7cyeDgIIWFhdTX1/P1r389UpMC1tTUsHnzZh577LHJK8gnXmejwzBb4jLJosSHOrMldKtWreLRRx/lySefZGhoiCeffJJHH32UVatWhZ1amvXr13PHHXdQX1/Pu+++S319PXfccUfaSJMoiMt06LIwqFBIzhw5coRNmzZRUFDApk2bOHLkSNgpHaWzs5Obb76ZnTt38oEPfICdO3dy880309nZGXZqaXTjIsklNT1JTmQaChulv8E4Nem0t7fT3Nw82UTW1NSk6VDkhKnpSSLBzNixYwf9/f3s2LEjktdRxKlJp66ujp6eHkZHR+np6VGRkHmjQiE5s2zZMs455xzy8/M555xzWLZsWdgpHUVNOiJHU6GQnFmzZk3aBXdr1qwJO6Wj6F7UJyddvHgMM42bne0CJIHngH8LXpcCu4GXg8eSlHW3A68ALwGbUuJrgeeD977K7/tOCoHvBPFngIpj5aPrKKLJzBzwkpISTyQSXlJS4oCbWdipyUlOFy+OI8N1FNk4o7geSJ1s5BbgaXc/E3g6eI2ZnQVsAaqBS4B7zCwZbHMvcBVwZrBcEsQbgEPu/hHgK8AdWchXQlBcXAzAoUOHGBsb49ChQ2lxOX46Cs4OTYE/CzNVkNksQDnjxeAifn9G8RKwMni+EnjJf382sT1l26eAjwXr/CIlXgd8PXWd4Hke8FuCs42ZFp1RRBPgBQUFDkwuE6/l+OkoOHvicjX+fGMezyj+AbgJGEuJrXD3/UER2g+cHsTLgNT5pPuCWFnwfGo8bRt3HwHeAU6dmoSZXWVm3WbWfeDAgTn+SDJf8vPzqaiowMyoqKhIG4IaJXE4UtdRcPbEaaRbWE64UJjZnwFvufuzs91kmphniGfaJj3gfr+717p77fLly2eZjuTa7373OxobG3nvvfdobGzkd7/7XdgpHSUus8eGOeX0QtPU1MRnP/tZKisrSSaTVFZW8tnPflYj3VLM5Yzi48Cfm9le4NvARWb2L8BvzGwlQPD4VrB+H7A6Zfty4I0gXj5NPG0bM8sDTgEOziFnCVFRUREtLS0sWbKElpYWioqKwk7pKHE5UtdR8PzwCF38GSkztUkdzwL8Mb/vo/jfwC3B81uAO4Pn1cB/MT6SqRL4FZAM3vsJcAHjZxBPAn8axK8F7guebwEePlYu6qOIJsCXLl3qFRUVbmZeUVHhS5cujVwfRVzaq9VHkT2aiXcc8zzqaarbgQ1m9jKwIXiNu78APAy8CHwfuNbdJ+ZEuAb4BuPDYPcExQKgFTjVzF4B/oZgBJXET3l5OYlE+p9bIpGgvLx8hi3CUVVVxW233ZbWR3HbbbdF7ki9rq6OT3ziE2nXpXziE5+I5PUeUe/zUTPeLMxUQeK66Iwimtra2nz58uVeUVHhiUTCKyoqfPny5ZE7At62bZvn5eX5jh07vL+/33fs2OF5eXm+bdu2sFNLE5czijjkqTOKcejGRRIWUobDHmuJgurqam9qakq7OczE6yiJy5dbHH6fcShmuaBCIZESlaIwnbj0USQSCX/ggQfSvoAfeOCByOU50R+V+iU80U8VJfN557i4UKGQSIlyoYjLkXpc7kFeWFjoO3bsSIvt2LHDCwsLQ8pIZpKpUGhSQJEUcZo9dmBggPr6eoqKiqivr2dgYCDslI4yNDTE1772tbTf59e+9jWGhobCTk2OQ17YCYhEycSoocbGxskbAkVx9th9+/Zx2mmnAb8f+5+fn8++ffvCTOsoZ511Fps3b077fV5++eU89thjYacmx0GFQmSKurq6yBWGqQoKClizZg3d3d24O/v376e2tjZyt5dtamqiqamJ1tZW1q1bR1dXFw0NDZG7gFEyU9OTSAwNDg7ygx/8gPr6eg4fPkx9fT0/+MEPGBwcDDu1NLq/x8Kge2ZLzpkZC+3vLtcSiQQXXXQRb7755mSTzhlnnEFHRwdjY2PH3oHIFLpntsgC4+7s2bMnbfLCPXv2qADLvFChEJki6lNOABQWFlJWVpY2hUdZWRmFhYVhpyYLkAqFSIr29nauv/56+vv7Aejv7+f666+PXLG48MILp+2juPDCC8NOTRYg9VFIzkW5j2L16tWMjo7y0EMPTY7S+Yu/+AuSySSvv/76sXeQIzU1Nbz//vu8+uqrk7HKykqKi4vp6ekJMTOJK/VRiMxSX18fV155ZdoonSuvvJK+vr5jb5xDL774Inv37mXFihWYGStWrGDv3r28+OKLYacmC5AKhcgU99xzD/39/bg7/f393HPPPWGndBR3Z8mSJbS3tzM4OEh7eztLliyJ7JmaxJsKhUiKZDLJkSNH0m7ZeuTIEZLJZNipHWXx4sUZX4tkiwqFSIrR0VGWLl2adsvWpUuXMjo6euyNQ3DRRRdRUFDARRddFHYqsRaHkW5hUqEQmeLCCy9k//79k1NjRHEkUV5eHm+++SZmBowPEHjzzTfJy9OsPMervb2dpqamtGtSmpqaVCxSqFCIpCgtLeWJJ57gy1/+Mv39/Xz5y1/miSeeoLS0NOzU0oyMjACkFYrUuMxec3Mzra2trF+/nvz8fNavX09ra6vmo0qh4bGSc1EfHvv2228zMjLC8PAw+fn55OXlceqpp0ZqeKyZ8eEPf5hf/epXuDtmxoc+9CFdnX0CkskkAwMD5OfnT8aGh4cpKiqKbJPjfNDwWJFZ2rdvH0uWLKGsrAwzo6ysjCVLlkRu+m6APXv2cNddd9Hf389dd93Fnj17wk4plqqqqvjMZz5DUVERZkZRURGf+cxnqKqqCju1yFChEElRUFDAxo0bWbx4MWbG4sWL2bhxIwUFBWGnNq0bb7yRxYsXc+ONN4adSmyVlZXx2GOPpV3l/thjj1FWVhZ2apGhpifJuSg3PSUS48dOp59+Om+99dbkIxCpWVkn+iSmE9XfbVQVFRVx2WWX8bOf/WxyJt6zzz6bRx55JJJ3DZwvmZqeNERCJEUymaSwsJBFixYBsGjRIoqLiyN3nweAD37wg7z55psMDg5SWFjIGWecwa9//euw04qdwcFB7r//foqLiydj77//Pg899FCIWUWLmp5EUoyMjDA2Nsa+fftwd/bt28fY2FgkRxPt27cvbXRWFPtR4qCwsJD77rsvLXbfffdpJt4UOqMQmWJgYGCyCWpsbCxSzQ+pTU4jIyPccMMN3HDDDdOuoyao2fniF7/IzTffDMDWrVu57777uPnmm9m6dWvImUWHzihEpnB3TjvtNMyM0047LVJfuO6Ou7Nt2zby8vLYsWMHADt27CAvL49t27ZNriOz09LSwtatW7n11ltZvHgxt956K1u3bqWlpSXs1CJDndmSc1HuzI5TJ3FjYyP/9E//NNlH8cUvflFfbnLC5uU6CjNbbWadZtZrZi+Y2fVBvNTMdpvZy8FjSco2283sFTN7ycw2pcTXmtnzwXtfteB/q5kVmtl3gvgzZlZxovmKHI/U6bujamLKCWBy6gk5MZrrKbO5ND2NADe4exVwAXCtmZ0F3AI87e5nAk8Hrwne2wJUA5cA95jZxJSc9wJXAWcGyyVBvAE45O4fAb4C3DGHfEVmxcy46aabeO+997jpppsynmVI/MXlroahmmjPnOsCfA/YALwErAxiK4GXgufbge0p6z8FfCxY5xcp8Trg66nrBM/zgN8SNJfNtKxdu9Yl2sb/7KIJ8Pz8fAcml4nXURXl3Nzd29ravLq62hOJhFdXV3tbW1vYKaUpLy/34uLiyX/n/Px8Ly4u9vLy8rBTyymg22f4Xs1KZ3bQJHQO8Aywwt33B0VoP3B6sFoZkDpZTl8QKwueT42nbePuI8A7wKnTfP5VZtZtZt0HDhzIxo8kJ6lEIsHw8PDk/SeSySTDw8OTo6Dk+LS3t3P11Vfzy1/+krGxMX75y19y9dVXR+pova+vj8HBQW6//Xb6+/u5/fbbGRwcjNxdDcM0579+M1sC/CvwV+5+JNOq08Q8QzzTNukB9/vdvdbda5cvX36slEVmNHGh3dKlS9MeJ+JyfLZt28b777+f9iX8/vvvs23btrBTS3PhhReyc+dOPvCBD7Bz585ITi0fpjkVCjPLZ7xIPOTu3w3CvzGzlcH7K4G3gngfsDpl83LgjSBePk08bRszywNOAQ7OJWeRTPr7+6moqODQoUMAHDp0iIqKisn2azk+Bw8e5Pzzz08benr++edz8GC0/ht3dHTw9ttvA/D222/T0dERckbRMpdRTwa0Ar3u/vcpbz0OXBE8v4LxvouJ+JZgJFMl453WPw6ap941swuCfX5+yjYT+7oM6Aja0kTmzd69e9Oanvbu3RtuQjH3wx/+kGXLlgGwbNkyfvjDH4ab0DQmhmyPjY1NTtseRWGNzprLldkfBz4HPG9mPwtitwK3Aw+bWQPwGvBpAHd/wcweBl5kfMTUte4+Mdn7NcA3gUXAk8EC44XoQTN7hfEziS1zyFdk1iaOR3Rckh1mRiKRiOwX8IQo5zdxJ77W1lbWrVtHV1cXDQ0NANTV1c3vh8/Uyx3XRaOewlNSUpI2WigbS0lJSU5/htTPTSQSaT9TVEU9t5mWqAD83HPPdTNzwM3Mzz333Ejl6O5eXV3tHR0dabGOjg6vrq7Oyv6Z71FPIjDenj/TH9qJLhN9Bbl09tlns2rVKgBWrVrF2WefnfMcFprUpryoKS0t5bnnnmPFihUkEglWrFjBc889F7nb3/b29tLX15fW9NTX10dvb++8f7YKhcgUP//5z6mvr+fdd9+lvr6en//852GnFHt33nkn/f393HnnnWGnMqMDBw4wNjZGVIfYr1q1iuuuuy7twsDrrrtu8qBmPmmuJ8ma+ZjDKVfzQh1P23TU/s9Efe6svLy8tGnaJ15HJeeJ/pPUG1NNvI5KjgCnnnoq77zzDnfeeefkLLc33XQTp5xyyuSIrbnQPbNFjmGiqWvbtm2TX24w/qVmZpqV9QQlEomj7uUxMjISuQsYx8bGuOaaazh8+DDXXHNNpO5mOOHgwYPceOONadd73HjjjTkZaqwzCsmaOJ9RpIrbrKxRPqMoKipicHBwMseJx8LCwsjc5yMuMwabGbt27WLDhg2Tsd27d7Nx48as5JnpjEKFQrJmoRSKKHz28YhynmZGfn4+AMPDw2nPo5JzXArF6tWrGRkZoa2tbXJ47OWXX05eXh6vv/76sXdwDGp6EpHQrF27drKpKZFIsHbt2pAzml5qjlF05513Mjo6Sn19PYWFhdTX1zM6OpqTAQLR/I2IyILxox/9iOLiYgCKi4v50Y9+FHJG0ysuLsbMJnONmrq6Ou6++24WL16MmbF48WLuvvvu+b/YDjU9SRap6SkcUc4zDs06ZkYymWR0dHQyNvE6KjnmgpqeREQySC0S070+2alQiERQaWkpZjarBZjVelG70ljiQ4VCJIIWynQocZFMJidHZOXn50dyqhEIb/ZYFQoROel96lOfYs2aNSQSCdasWcOnPvWpsFM6Suq9vd09p/f2Vme2ZI06s6P92WH8PFHuzD7eKcXDzjfM6yjmcj8KkTT+paXwt6dkf58i82Dii3/Tpk3s2rVrcn6niceNGzfy1FNPhZzl7/X19bF9+3YaGxvp7e2lqqqKL3zhC/zd3/3dvH+2zigkaxbKUfBC/ewoTrAI4R+pw3ix2L179+Q0Ixs2bIhUkYDx32tBQQFDQ0OTsYnX8z2Fh84o5KRSWlp6XJ26s/nSKykpidw9oMOU+qUV5aanVBNFwcwiOSHghNQiMd3r+aJCISeVidFE2RTl22eGrbS0dNoiqqG68aJRTyIyb95+++2jikJpaWlW7p8guaMzChGZVxNFIcpTjcRJQUHB5Ey8uWp60hmFiEiMFBQU4O4UFBTk7DN1RiESQRpqLDN577330h5zQYVCJILstiPzMzz2b7O6y9g4ntFusx2ccDKNdlOhkKzK9gigkpKSrO5PR+rZFZfhxnEe7Tabz0ldZz76gVQoJGtm+wca6oVsOlLPqjh/AcfFxO+3vb2dz33uc0fdN+PBBx+c95sXqTNbJKJmO834bJdsn51JbtXV1fHggw9SXV0NQHV1dU6KBMRkCg8zuwS4G0gC33D322daV1N4RF/YU2NkW9ht1aEOO81yM97v9/tOVncXlylRjrcpbzZm+/cZ6yk8zCwJ/COwAegDfmJmj7v7i+FmJnF0PP+xNe7/2OLSlBeXvqmD140C2d7v3O/WF/lCAZwHvOLuvwIws28DnwRUKEQiIOoDGGC8oGVbSUkJB/82u/uMap5xKBRlQOpk633A+akrmNlVwFUAf/AHfzD7PcfktDk2eU5jpi+R6eJhHr0rzxMz3WdEcfZY5Tk3cSgU0/2W0n4T7n4/cD+M91HMes85+KLMirjkOY24NN0oz+yJQ46gPI9HHEY99QGrU16XA2+ElIuIyEknDoXiJ8CZZlZpZgXAFuDxkHMSETlpRL7pyd1HzGwb8BTjw2N3uvsLIaclInLSiHyhAHD3fwf+Pew8RERORnFoehIRkRCpUIiISEYqFCIikpEKhYiIZBSLSQGPh5kdAH6d5d2eBvw2y/ucD8ozu5RndsUhzzjkCPOT5wfdffl0byy4QjEfzKx7plkVo0R5ZpfyzK445BmHHCH3earpSUREMlKhEBGRjFQoZuf+sBOYJeWZXcozu+KQZxxyhBznqT4KERHJSGcUIiKSkQqFiIhkpEKRgZntNLO3zKwn7FwyMbPVZtZpZr1m9oKZXR92TtMxsyIz+7GZ/VeQ521h5zQTM0ua2XNm9m9h5zITM9trZs+b2c/MrDvsfGZiZsvM7BEz+0XwN/qxsHOaysw+GvweJ5YjZvZXYec1HTP76+D/T4+ZtZtZ0bx/pvooZmZmfwS8Bzzg7jVh5zMTM1sJrHT3n5rZB4Bngc3uHqn7itv4PR0Xu/t7ZpYPdAHXu/uPQk7tKGb2N0AtsNTd/yzsfKZjZnuBWneP9AViZvYt4P+5+zeCe8oUu/vhkNOakZklgX3A+e6e7Yt358TMyhj/f3OWu//OzB4G/t3dvzmfn6szigzc/T+Ag2HncSzuvt/dfxo8fxfoZfxe45Hi494LXuYHS+SOVMysHPgE8I2wc4k7M1sK/BHQCuDuQ1EuEoGLgT1RKxIp8oBFZpYHFJODO36qUCwwZlYBnAM8E3Iq0wqadH4GvAXsdvco5vkPwE3AWMh5HIsDu8zsWTO7KuxkZvAh4ADwz0FT3jfMbHHYSR3DFqA97CSm4+77gLuA14D9wDvuvmu+P1eFYgExsyXAvwJ/5e5Hws5nOu4+6u5nM37v8/PMLFJNemb2Z8Bb7v5s2LnMwsfd/VzgUuDaoKk0avKAc4F73f0coB+4JdyUZhY0jf058H/CzmU6ZlYCfBKoBFYBi83sL+f7c1UoFoigzf9fgYfc/bth53MsQfPD/wUuCTeTo3wc+POg/f/bwEVm9i/hpjQ9d38jeHwLeBQ4L9yMptUH9KWcOT7CeOGIqkuBn7r7b8JOZAZ/Arzq7gfcfRj4LvCH8/2hKhQLQNBJ3Ar0uvvfh53PTMxsuZktC54vYvyP/hehJjWFu29393J3r2C8CaLD3ef9iO14mdniYOACQVPORiByo/Pc/U3gdTP7aBC6GIjUIIsp6ohos1PgNeACMysO/t9fzHif5LxSocjAzNqB/wQ+amZ9ZtYQdk4z+DjwOcaPfieG9/1p2ElNYyXQaWY/B37CeB9FZIefRtwKoMvM/gv4MfCEu38/5Jxm0gg8FPy7nw18Odx0pmdmxcAGxo/SIyk4M3sE+CnwPOPf4fM+nYeGx4qISEY6oxARkYxUKEREJCMVChERyUiFQkREMlKhEBGRjFQoREQkIxUKERHJ6P8DpE3DECbxNtcAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.boxplot(customers.values)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Channel False\n", + "Region False\n", + "Fresh False\n", + "Milk False\n", + "Grocery True\n", + "Frozen False\n", + "Detergents_Paper True\n", + "Delicassen False\n", + "dtype: bool" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cc = customers.corr()\n", + "cc[(cc > 0.8) & (cc < 1)].any()\n", + "# Grocery x Detergents_Paper" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/vy/72w35wd12ms4_h7n214795k40000gn/T/ipykernel_10644/4237668849.py:6: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " mask = np.zeros_like(corr, dtype=np.bool)\n" + ] + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " Fresh Milk Grocery Frozen Detergents_Paper Delicassen Channel_1 \\\n", + "0 12669 9656 7561 214 2674 1338 0 \n", + "1 7057 9810 9568 1762 3293 1776 0 \n", + "2 6353 8808 7684 2405 3516 7844 0 \n", + "3 13265 1196 4221 6404 507 1788 1 \n", + "4 22615 5410 7198 3915 1777 5185 0 \n", + "\n", + " Channel_2 Region_1 Region_2 Region_3 \n", + "0 1 0 0 1 \n", + "1 1 0 0 1 \n", + "2 1 0 0 1 \n", + "3 0 0 0 1 \n", + "4 1 0 0 1 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here" + "categorical_features = ['Channel', 'Region']\n", + "other_features = ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']\n", + "\n", + "for col in categorical_features:\n", + " dummies = pd.get_dummies(customers[col], prefix=col) #we want to create dummies for each categorical feature\n", + " customers = pd.concat([customers, dummies], axis=1) #we want to concatenate the dummies created to the original dataframe customer, along the columns\n", + " customers.drop(col, axis=1, inplace=True) #we want to drop the original columns which are classified as categorical features, since dummies were created \n", + " #for each of their unique values\n", + " \n", + "customers.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/vy/72w35wd12ms4_h7n214795k40000gn/T/ipykernel_10644/1344249050.py:5: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " mask = np.zeros_like(corr, dtype=np.bool)\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap to show correlation among the columns of our dataframe, after having dropped the original categorical_features\n", + "corr = np.abs(customers.corr())\n", + "\n", + "#Set up mask for triangle representation\n", + "mask = np.zeros_like(corr, dtype=np.bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "\n", + "# Set up the matplotlib figure\n", + "f, ax = plt.subplots(figsize=(14, 14))\n", + "\n", + "# Generate a custom diverging colormap\n", + "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", + "\n", + "# Draw the heatmap with the mask and correct aspect ratio\n", + "sns.heatmap(corr, mask=mask, vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ - "# Your comment here" + "# When looking at the new coorelation matrix, we can see that the Region doesn't afect our features in a considerable way therefore we decide to drop these columns from our\n", + "# new dataframe, customers_transformed\n", + "\n", + "columns_to_drop = ['Region_1','Region_2','Region_3']" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FreshMilkGroceryFrozenDetergents_PaperDelicassenChannel_1Channel_2
012669965675612142674133801
170579810956817623293177601
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" + ], + "text/plain": [ + " Fresh Milk Grocery Frozen Detergents_Paper Delicassen Channel_1 \\\n", + "0 12669 9656 7561 214 2674 1338 0 \n", + "1 7057 9810 9568 1762 3293 1776 0 \n", + "2 6353 8808 7684 2405 3516 7844 0 \n", + "3 13265 1196 4221 6404 507 1788 1 \n", + "4 22615 5410 7198 3915 1777 5185 0 \n", + "\n", + " Channel_2 \n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 0 \n", + "4 1 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers_transformed = customers.drop((columns_to_drop), axis=1)\n", + "customers_transformed.head()" ] }, { @@ -127,15 +1269,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# Your import here:\n", "\n", - "from sklearn.preprocessing import StandardScaler\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardScaler()" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create an empty model\n", "\n", - "# Your code here:\n" + "scaler = StandardScaler()\n", + "\n", + "# Train the model with dataset customers_transformed\n", + "scaler.fit(customers_transformed)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.05293319, 0.52356777, -0.04111489, ..., -0.06633906,\n", + " -1.44865163, 1.44865163],\n", + " [-0.39130197, 0.54445767, 0.17031835, ..., 0.08915105,\n", + " -1.44865163, 1.44865163],\n", + " [-0.44702926, 0.40853771, -0.0281571 , ..., 2.24329255,\n", + " -1.44865163, 1.44865163],\n", + " ...,\n", + " [ 0.20032554, 1.31467078, 2.34838631, ..., 0.12145607,\n", + " -1.44865163, 1.44865163],\n", + " [-0.13538389, -0.51753572, -0.60251388, ..., 0.21304614,\n", + " 0.69029709, -0.69029709],\n", + " [-0.72930698, -0.5559243 , -0.57322717, ..., -0.52286938,\n", + " 0.69029709, -0.69029709]])" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customer_scaler = scaler.transform(customers_transformed)\n", + "customer_scaler" ] }, { @@ -149,13 +1350,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": { "scrolled": true }, "outputs": [], "source": [ - "# Your code here:\n" + "from sklearn.cluster import KMeans\n", + "\n", + "kmeans = KMeans()\n", + "kmeans.fit(customer_scaler)\n", + "customers_transformed['labels'] = kmeans.labels_\n" ] }, { @@ -167,11 +1372,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "6 178\n", + "0 100\n", + "1 89\n", + "4 37\n", + "7 28\n", + "2 5\n", + "5 2\n", + "3 1\n", + "Name: labels, dtype: int64" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "customers_transformed['labels'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.21763541442593332" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import silhouette_score\n", + "\n", + "data = customers_transformed.drop(columns=['labels'], axis=1)\n", + "target = customers_transformed['labels']\n", + "\n", + "score = silhouette_score(data, target, metric='euclidean')\n", + "score" ] }, { @@ -185,13 +1435,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": { "scrolled": true }, "outputs": [], "source": [ - "# Your code here\n" + "from sklearn.cluster import DBSCAN\n", + "\n", + "dbscan_model = DBSCAN(eps=0.5)\n", + "dbscan_model.fit(customer_scaler)\n", + "customers_transformed['labels_DBSCAN'] = dbscan_model.labels_\n" ] }, { @@ -203,11 +1457,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + " 1 201\n", + "-1 194\n", + " 0 32\n", + " 3 8\n", + " 2 5\n", + "Name: labels_DBSCAN, dtype: int64" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "customers_transformed['labels_DBSCAN'].value_counts()" ] }, { @@ -234,11 +1504,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.21763541442593332\n" + ] + } + ], "source": [ - "# Your code here:\n" + "target_kmeans = customers_transformed['labels']\n", + "score_kmeans = silhouette_score(data, target_kmeans, metric='euclidean')\n", + "print(score_kmeans)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.1313098063387462\n" + ] + } + ], + "source": [ + "target_dbscan = customers_transformed['labels_DBSCAN']\n", + "score_dbscan = silhouette_score(data, target_dbscan, metric='euclidean')\n", + "print(score_dbscan)" ] }, { @@ -282,11 +1581,304 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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FreshMilkGroceryFrozenDetergents_PaperDelicassenChannel_1Channel_2
labelslabels_DBSCAN
0-110670.0166677177.03333311694.2500002024.0666674667.8333331768.9666670.01.0
04838.5000006644.59375010277.156250784.5937504776.0937501004.8437500.01.0
314726.6250006636.1250007003.625000961.3750002583.6250001951.8750000.01.0
1-117591.6571435847.8571435869.3714297605.971429819.5142861943.9714291.00.0
117735.6481481903.6481482483.4259264344.018519396.333333909.4814811.00.0
2-125603.00000043460.60000061472.2000002636.00000029974.2000002708.8000000.01.0
3-136847.00000043950.00000020170.00000036534.000000239.00000047943.0000001.00.0
4-16041.97297316435.18918924971.1081081817.54054111589.8648652203.6756760.01.0
5-122015.5000009937.0000007844.00000047939.000000671.5000004153.5000001.00.0
6-14942.1290325968.1935487165.0645162011.6129032187.6129031770.5483871.00.0
16510.1292522176.2380952747.1972791514.081633595.639456713.3469391.00.0
7-145390.3478266317.0000007327.5217398368.2173911150.6086963690.4782611.00.0
241446.6000001421.2000002167.6000001276.400000416.200000784.8000001.00.0
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" + ], + "text/plain": [ + " Fresh Milk Grocery Frozen \\\n", + "labels labels_DBSCAN \n", + "0 -1 10670.016667 7177.033333 11694.250000 2024.066667 \n", + " 0 4838.500000 6644.593750 10277.156250 784.593750 \n", + " 3 14726.625000 6636.125000 7003.625000 961.375000 \n", + "1 -1 17591.657143 5847.857143 5869.371429 7605.971429 \n", + " 1 17735.648148 1903.648148 2483.425926 4344.018519 \n", + "2 -1 25603.000000 43460.600000 61472.200000 2636.000000 \n", + "3 -1 36847.000000 43950.000000 20170.000000 36534.000000 \n", + "4 -1 6041.972973 16435.189189 24971.108108 1817.540541 \n", + "5 -1 22015.500000 9937.000000 7844.000000 47939.000000 \n", + "6 -1 4942.129032 5968.193548 7165.064516 2011.612903 \n", + " 1 6510.129252 2176.238095 2747.197279 1514.081633 \n", + "7 -1 45390.347826 6317.000000 7327.521739 8368.217391 \n", + " 2 41446.600000 1421.200000 2167.600000 1276.400000 \n", + "\n", + " Detergents_Paper Delicassen Channel_1 Channel_2 \n", + "labels labels_DBSCAN \n", + "0 -1 4667.833333 1768.966667 0.0 1.0 \n", + " 0 4776.093750 1004.843750 0.0 1.0 \n", + " 3 2583.625000 1951.875000 0.0 1.0 \n", + "1 -1 819.514286 1943.971429 1.0 0.0 \n", + " 1 396.333333 909.481481 1.0 0.0 \n", + "2 -1 29974.200000 2708.800000 0.0 1.0 \n", + "3 -1 239.000000 47943.000000 1.0 0.0 \n", + "4 -1 11589.864865 2203.675676 0.0 1.0 \n", + "5 -1 671.500000 4153.500000 1.0 0.0 \n", + "6 -1 2187.612903 1770.548387 1.0 0.0 \n", + " 1 595.639456 713.346939 1.0 0.0 \n", + "7 -1 1150.608696 3690.478261 1.0 0.0 \n", + " 2 416.200000 784.800000 1.0 0.0 " + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "customers_transformed.groupby(['labels','labels_DBSCAN']).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Fresh Milk Grocery Frozen \\\n", + "labels \n", + "0 9128.460000 6963.380000 10865.530000 1542.420000 \n", + "1 17679.022472 3454.741573 3814.977528 5626.808989 \n", + "2 25603.000000 43460.600000 61472.200000 2636.000000 \n", + "3 36847.000000 43950.000000 20170.000000 36534.000000 \n", + "4 6041.972973 16435.189189 24971.108108 1817.540541 \n", + "5 22015.500000 9937.000000 7844.000000 47939.000000 \n", + "6 6237.050562 2836.634831 3516.601124 1600.730337 \n", + "7 44686.107143 5442.750000 6406.107143 7101.821429 \n", + "\n", + " Detergents_Paper Delicassen Channel_1 Channel_2 labels_DBSCAN \n", + "labels \n", + "0 4535.740000 1539.080000 0.0 1.0 -0.360000 \n", + "1 562.752809 1316.303371 1.0 0.0 0.213483 \n", + "2 29974.200000 2708.800000 0.0 1.0 -1.000000 \n", + "3 239.000000 47943.000000 1.0 0.0 -1.000000 \n", + "4 11589.864865 2203.675676 0.0 1.0 -1.000000 \n", + "5 671.500000 4153.500000 1.0 0.0 -1.000000 \n", + "6 872.893258 897.466292 1.0 0.0 0.651685 \n", + "7 1019.464286 3171.607143 1.0 0.0 -0.464286 \n", + " Fresh Milk Grocery Frozen \\\n", + "labels_DBSCAN \n", + "-1 14873.912371 9560.984536 13221.046392 4408.881443 \n", + " 0 4838.500000 6644.593750 10277.156250 784.593750 \n", + " 1 9525.940299 2103.004975 2676.333333 2274.363184 \n", + " 2 41446.600000 1421.200000 2167.600000 1276.400000 \n", + " 3 14726.625000 6636.125000 7003.625000 961.375000 \n", + "\n", + " Detergents_Paper Delicassen Channel_1 Channel_2 labels \n", + "labels_DBSCAN \n", + "-1 5068.613402 2398.324742 0.474227 0.525773 2.850515 \n", + " 0 4776.093750 1004.843750 0.000000 1.000000 0.000000 \n", + " 1 542.094527 766.039801 1.000000 0.000000 4.656716 \n", + " 2 416.200000 784.800000 1.000000 0.000000 7.000000 \n", + " 3 2583.625000 1951.875000 0.000000 1.000000 0.000000 \n" + ] + } + ], + "source": [ + "print(customers_transformed.groupby('labels').mean())\n", + "\n", + "print(customers_transformed.groupby('labels_DBSCAN').mean())" ] }, { @@ -363,6 +1955,9 @@ } ], "metadata": { + "interpreter": { + "hash": "308f656801bdbd0b2d403197f25506e92fef2516366746747ba806d876d6bcae" + }, "kernelspec": { "display_name": "Python 3", "language": "python", @@ -378,7 +1973,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.7.6" } }, "nbformat": 4,