From 74db9439a4211025d3bd7dfc5eafbb72bc2c9299 Mon Sep 17 00:00:00 2001 From: Dilrose Reji Date: Fri, 6 Aug 2021 23:23:47 +0530 Subject: [PATCH 1/4] data pre processing in ml --- .../Handling_categorical_data.ipynb | 123 ++++++++ .../Handling_numerical_data1.ipynb | 269 ++++++++++++++++++ .../Data Preprocessing in ML/README.md | 36 +++ .../Data Preprocessing in ML/data.csv | 8 + .../place_area_price.csv | 9 + 5 files changed, 445 insertions(+) create mode 100644 Machine Learning/Libraries/Data Preprocessing in ML/Handling_categorical_data.ipynb create mode 100644 Machine Learning/Libraries/Data Preprocessing in ML/Handling_numerical_data1.ipynb create mode 100644 Machine Learning/Libraries/Data Preprocessing in ML/README.md create mode 100644 Machine Learning/Libraries/Data Preprocessing in ML/data.csv create mode 100644 Machine Learning/Libraries/Data Preprocessing in ML/place_area_price.csv diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/Handling_categorical_data.ipynb b/Machine Learning/Libraries/Data Preprocessing in ML/Handling_categorical_data.ipynb new file mode 100644 index 00000000..41037b79 --- /dev/null +++ b/Machine Learning/Libraries/Data Preprocessing in ML/Handling_categorical_data.ipynb @@ -0,0 +1,123 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "source": [ + "# handling categorical data using dummies from pandas\r\n", + "\r\n", + "import pandas as pd" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 2, + "source": [ + "data = pd.read_csv(\"place_area_price.csv\")\r\n", + "print(data)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " place area price\n", + "0 Lonavala 2600 550000\n", + "1 Lonavala 3000 565000\n", + "2 Lonavala 3200 610000\n", + "3 Karjat 2600 450000\n", + "4 Karjat 2800 500000\n", + "5 Khandala 2600 600000\n", + "6 Khandala 2900 650000\n", + "7 Khandala 3200 680000\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 3, + "source": [ + "dummies = pd.get_dummies(data.place)\r\n", + "print(dummies)\r\n", + "\r\n", + "# the data is converted to numerical values but here we can see that if one col is deleted,\r\n", + "# we can predict the value for other cols\r\n", + "# eg1: if we drop the first row, and if it has to be 1 then the other 2 cols would have values 0\r\n", + "# eg2: if lonavala has 1, then we can say that khandala and karjat would be 0 and hence it is ok to,\r\n", + "# drop the first col which would become easy for the model to compute" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Karjat Khandala Lonavala\n", + "0 0 0 1\n", + "1 0 0 1\n", + "2 0 0 1\n", + "3 1 0 0\n", + "4 1 0 0\n", + "5 0 1 0\n", + "6 0 1 0\n", + "7 0 1 0\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "source": [ + "new_dummies = pd.get_dummies(data.place, drop_first=True)\r\n", + "print(new_dummies)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Khandala Lonavala\n", + "0 0 1\n", + "1 0 1\n", + "2 0 1\n", + "3 0 0\n", + "4 0 0\n", + "5 1 0\n", + "6 1 0\n", + "7 1 0\n" + ] + } + ], + "metadata": {} + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python", + "version": "3.6.4", + "mimetype": "text/x-python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3.6.4 64-bit" + }, + "interpreter": { + "hash": "f2db1a205d05422567bfea71378eb1163d4d5d7418f0062693a7c6bfcbaf4348" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/Handling_numerical_data1.ipynb b/Machine Learning/Libraries/Data Preprocessing in ML/Handling_numerical_data1.ipynb new file mode 100644 index 00000000..85af764d --- /dev/null +++ b/Machine Learning/Libraries/Data Preprocessing in ML/Handling_numerical_data1.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "source": [ + "# check for null values\r\n", + "\r\n", + "import pandas as pd\r\n", + "data = pd.read_csv(\"data.csv\")\r\n", + "print(data)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Name Age Position Experience Salary\n", + "0 Nitin 24.0 ML Engg 4.0 22000.0\n", + "1 Harshita 23.0 ML Engg 3.0 18000.0\n", + "2 Arya NaN Data Analyst NaN NaN\n", + "3 Gagan NaN Data Analyst 2.0 10000.0\n", + "4 Randhir NaN NaN 4.0 16000.0\n", + "5 Saurav 22.0 admin 3.0 8000.0\n", + "6 Manoj 31.0 Manager 11.0 25000.0\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 2, + "source": [ + "# check for null data\r\n", + "res = data.isnull().sum()\r\n", + "print(res)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Name 0\n", + "Age 3\n", + "Position 1\n", + "Experience 1\n", + "Salary 1\n", + "dtype: int64\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 3, + "source": [ + "# find out all whose age is null\r\n", + "d1 = data[data.Age.isnull()]\r\n", + "print(d1)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Name Age Position Experience Salary\n", + "2 Arya NaN Data Analyst NaN NaN\n", + "3 Gagan NaN Data Analyst 2.0 10000.0\n", + "4 Randhir NaN NaN 4.0 16000.0\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "source": [ + "# if any value is null\r\n", + "d3 = data[data.isnull().any(axis=1)]\r\n", + "print(d3)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Name Age Position Experience Salary\n", + "2 Arya NaN Data Analyst NaN NaN\n", + "3 Gagan NaN Data Analyst 2.0 10000.0\n", + "4 Randhir NaN NaN 4.0 16000.0\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### How to drop ?" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 5, + "source": [ + "# drops if any null values are present\r\n", + "d1 = data.dropna(how=\"any\", axis=0)\r\n", + "print(d1)\r\n", + "\r\n", + "# arya, gagan, randhir got dropped" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Name Age Position Experience Salary\n", + "0 Nitin 24.0 ML Engg 4.0 22000.0\n", + "1 Harshita 23.0 ML Engg 3.0 18000.0\n", + "5 Saurav 22.0 admin 3.0 8000.0\n", + "6 Manoj 31.0 Manager 11.0 25000.0\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 6, + "source": [ + "# drop whose salary is null\r\n", + "d2 = data.dropna(subset=[\"Salary\"])\r\n", + "print(d2)\r\n", + "\r\n", + "# arya got dropped" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Name Age Position Experience Salary\n", + "0 Nitin 24.0 ML Engg 4.0 22000.0\n", + "1 Harshita 23.0 ML Engg 3.0 18000.0\n", + "3 Gagan NaN Data Analyst 2.0 10000.0\n", + "4 Randhir NaN NaN 4.0 16000.0\n", + "5 Saurav 22.0 admin 3.0 8000.0\n", + "6 Manoj 31.0 Manager 11.0 25000.0\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "### How to fill missing values ?" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 7, + "source": [ + "d1 = data.fillna({\"Age\": data[\"Age\"].mean()})\r\n", + "print(d1)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Name Age Position Experience Salary\n", + "0 Nitin 24.0 ML Engg 4.0 22000.0\n", + "1 Harshita 23.0 ML Engg 3.0 18000.0\n", + "2 Arya 25.0 Data Analyst NaN NaN\n", + "3 Gagan 25.0 Data Analyst 2.0 10000.0\n", + "4 Randhir 25.0 NaN 4.0 16000.0\n", + "5 Saurav 22.0 admin 3.0 8000.0\n", + "6 Manoj 31.0 Manager 11.0 25000.0\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 8, + "source": [ + "# fill missing values in Experience\r\n", + "d2 = data.fillna({\"Experience\": data[\"Experience\"].mean()})\r\n", + "print(d2)\r\n", + "print(\" \")" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Name Age Position Experience Salary\n", + "0 Nitin 24.0 ML Engg 4.0 22000.0\n", + "1 Harshita 23.0 ML Engg 3.0 18000.0\n", + "2 Arya NaN Data Analyst 4.5 NaN\n", + "3 Gagan NaN Data Analyst 2.0 10000.0\n", + "4 Randhir NaN NaN 4.0 16000.0\n", + "5 Saurav 22.0 admin 3.0 8000.0\n", + "6 Manoj 31.0 Manager 11.0 25000.0\n", + " \n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "source": [ + "# filling null values in Position by a default one\r\n", + "d3 = data.fillna({\"Position\": \"Unallocated\"})\r\n", + "print(d3)\r\n" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Name Age Position Experience Salary\n", + "0 Nitin 24.0 ML Engg 4.0 22000.0\n", + "1 Harshita 23.0 ML Engg 3.0 18000.0\n", + "2 Arya NaN Data Analyst NaN NaN\n", + "3 Gagan NaN Data Analyst 2.0 10000.0\n", + "4 Randhir NaN Unallocated 4.0 16000.0\n", + "5 Saurav 22.0 admin 3.0 8000.0\n", + "6 Manoj 31.0 Manager 11.0 25000.0\n" + ] + } + ], + "metadata": {} + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python", + "version": "3.6.4", + "mimetype": "text/x-python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3.6.4 64-bit" + }, + "interpreter": { + "hash": "f2db1a205d05422567bfea71378eb1163d4d5d7418f0062693a7c6bfcbaf4348" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/README.md b/Machine Learning/Libraries/Data Preprocessing in ML/README.md new file mode 100644 index 00000000..4a71a235 --- /dev/null +++ b/Machine Learning/Libraries/Data Preprocessing in ML/README.md @@ -0,0 +1,36 @@ +### Data Pre-processing in ML + +Data pre-processing means to understand the data. +- Data preprocessing is a process of preparing the raw data and making it suitable for a machine learning model. +- Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. +- Data preprocessing is a proven method of resolving such issues. + +It is of 2 types: +- one handle numerical values +- other to handle categorical values + +Our ML model cannot work with either null values or categorical values + +*---------------------------------Handling missing values----------------------------------* + +If you have less missing values and are not that important, then you can directly drop them +**Syntax:** +d1 = data.dropna(how="any", axis=0), it drops rows which as any null values +d2 = data.dropna(subset=[column_name]), drops rows from the column name specified + +If you want to fill missing values, then you can do them as following: +**Syntax:** +d1 = data.fillna({"Age": data["Age"].mean()}), for numerical data +d2 = data.fillna({"Position": "Unallocated"}), for categorical data + + +*------------------------------Handling categorical values-------------------------------* +As we know that our model cannot work with categorical values, we have to convert them into numerical data and this can be done using pandas library +**Syntax:** +features = pd.get_dummies(data.place) , eg: place is a column name in the data +It will convert the categorical data in 0,1,2.. format + +new_features = pd.get_dummies(data.place, drop_first=True) +About **drop_first=True**, i've explained in detailed in Handling_categorical_data.ipynb + + diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/data.csv b/Machine Learning/Libraries/Data Preprocessing in ML/data.csv new file mode 100644 index 00000000..8f00ee5e --- /dev/null +++ b/Machine Learning/Libraries/Data Preprocessing in ML/data.csv @@ -0,0 +1,8 @@ +Name,Age,Position,Experience,Salary +Nitin,24,ML Engg,4,22000 +Harshita,23,ML Engg,3,18000 +Arya,,Data Analyst,, +Gagan,,Data Analyst,2,10000 +Randhir,,,4,16000 +Saurav,22,admin,3,8000 +Manoj,31,Manager,11,25000 diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/place_area_price.csv b/Machine Learning/Libraries/Data Preprocessing in ML/place_area_price.csv new file mode 100644 index 00000000..ea161858 --- /dev/null +++ b/Machine Learning/Libraries/Data Preprocessing in ML/place_area_price.csv @@ -0,0 +1,9 @@ +place,area,price +Lonavala,2600,550000 +Lonavala,3000,565000 +Lonavala,3200,610000 +Karjat,2600,450000 +Karjat,2800,500000 +Khandala,2600,600000 +Khandala,2900,650000 +Khandala,3200,680000 From 6557eef5ae7c2fdacbdde34cec11cfeb8b0daba4 Mon Sep 17 00:00:00 2001 From: Dilrose Reji Date: Fri, 6 Aug 2021 23:31:08 +0530 Subject: [PATCH 2/4] updated --- .../Libraries/Data Preprocessing in ML/README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/README.md b/Machine Learning/Libraries/Data Preprocessing in ML/README.md index 4a71a235..07dc675e 100644 --- a/Machine Learning/Libraries/Data Preprocessing in ML/README.md +++ b/Machine Learning/Libraries/Data Preprocessing in ML/README.md @@ -11,6 +11,15 @@ It is of 2 types: Our ML model cannot work with either null values or categorical values + +*---------------------------------Check for null values----------------------------------* +In order to proceed, we need to check if our data has any null values or not +This can be checked with: +**Syntax:** +data.isnull().sum() +This will give no of null values in each columm + + *---------------------------------Handling missing values----------------------------------* If you have less missing values and are not that important, then you can directly drop them From 584b44b97339a7c7d3f288e1443b215cef2c5327 Mon Sep 17 00:00:00 2001 From: Dilrose Reji Date: Sun, 8 Aug 2021 11:05:10 +0530 Subject: [PATCH 3/4] updated --- .../{ => Dataset}/data.csv | 0 .../{ => Dataset}/place_area_price.csv | 0 .../Handling_categorical_data.ipynb | 12 ++-- .../Handling_numerical_data1.ipynb | 4 +- .../Data Preprocessing in ML/Images/img1.PNG | Bin 0 -> 32876 bytes .../Data Preprocessing in ML/Images/img2.PNG | Bin 0 -> 44609 bytes .../Data Preprocessing in ML/Images/img3.PNG | Bin 0 -> 57298 bytes .../Data Preprocessing in ML/Images/img4.PNG | Bin 0 -> 58773 bytes .../Data Preprocessing in ML/Images/img5.PNG | Bin 0 -> 26856 bytes .../Data Preprocessing in ML/README.md | 65 ++++++++++++++---- 10 files changed, 60 insertions(+), 21 deletions(-) rename Machine Learning/Libraries/Data Preprocessing in ML/{ => Dataset}/data.csv (100%) rename Machine Learning/Libraries/Data Preprocessing in ML/{ => Dataset}/place_area_price.csv (100%) create mode 100644 Machine Learning/Libraries/Data Preprocessing in ML/Images/img1.PNG create mode 100644 Machine Learning/Libraries/Data Preprocessing in ML/Images/img2.PNG create mode 100644 Machine Learning/Libraries/Data Preprocessing in ML/Images/img3.PNG create mode 100644 Machine Learning/Libraries/Data Preprocessing in ML/Images/img4.PNG create mode 100644 Machine Learning/Libraries/Data Preprocessing in ML/Images/img5.PNG diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/data.csv b/Machine Learning/Libraries/Data Preprocessing in ML/Dataset/data.csv similarity index 100% rename from Machine Learning/Libraries/Data Preprocessing in ML/data.csv rename to Machine Learning/Libraries/Data Preprocessing in ML/Dataset/data.csv diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/place_area_price.csv b/Machine Learning/Libraries/Data Preprocessing in ML/Dataset/place_area_price.csv similarity index 100% rename from Machine Learning/Libraries/Data Preprocessing in ML/place_area_price.csv rename to Machine Learning/Libraries/Data Preprocessing in ML/Dataset/place_area_price.csv diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/Handling_categorical_data.ipynb b/Machine Learning/Libraries/Data Preprocessing in ML/Handling_categorical_data.ipynb index 41037b79..798997e4 100644 --- a/Machine Learning/Libraries/Data Preprocessing in ML/Handling_categorical_data.ipynb +++ b/Machine Learning/Libraries/Data Preprocessing in ML/Handling_categorical_data.ipynb @@ -13,9 +13,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "source": [ - "data = pd.read_csv(\"place_area_price.csv\")\r\n", + "data = pd.read_csv(\"E:\\github\\DS-ScriptsNook\\Machine Learning\\Libraries\\Data Preprocessing in ML\\Dataset\\place_area_price.csv\") #load the data\r\n", "print(data)" ], "outputs": [ @@ -39,16 +39,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "source": [ - "dummies = pd.get_dummies(data.place)\r\n", + "dummies = pd.get_dummies(data.place) # dummies is a function in pandas to convert categorical values into numerical ones\r\n", "print(dummies)\r\n", "\r\n", "# the data is converted to numerical values but here we can see that if one col is deleted,\r\n", "# we can predict the value for other cols\r\n", "# eg1: if we drop the first row, and if it has to be 1 then the other 2 cols would have values 0\r\n", "# eg2: if lonavala has 1, then we can say that khandala and karjat would be 0 and hence it is ok to,\r\n", - "# drop the first col which would become easy for the model to compute" + "# drop the first col which would become easy for the model to compute. This is shown in the eg below" ], "outputs": [ { @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "source": [ "new_dummies = pd.get_dummies(data.place, drop_first=True)\r\n", "print(new_dummies)" diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/Handling_numerical_data1.ipynb b/Machine Learning/Libraries/Data Preprocessing in ML/Handling_numerical_data1.ipynb index 85af764d..39006155 100644 --- a/Machine Learning/Libraries/Data Preprocessing in ML/Handling_numerical_data1.ipynb +++ b/Machine Learning/Libraries/Data Preprocessing in ML/Handling_numerical_data1.ipynb @@ -7,7 +7,7 @@ "# check for null values\r\n", "\r\n", "import pandas as pd\r\n", - "data = pd.read_csv(\"data.csv\")\r\n", + "data = pd.read_csv(\"E:\\github\\DS-ScriptsNook\\Machine Learning\\Libraries\\Data Preprocessing in ML\\Dataset\\data.csv\")\r\n", "print(data)" ], "outputs": [ @@ -166,7 +166,7 @@ "cell_type": "code", "execution_count": 7, "source": [ - "d1 = data.fillna({\"Age\": data[\"Age\"].mean()})\r\n", + "d1 = data.fillna({\"Age\": data[\"Age\"].mean()}) # fillna fills a col with the mean/median values\r\n", "print(d1)" ], "outputs": [ diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/Images/img1.PNG b/Machine Learning/Libraries/Data Preprocessing in ML/Images/img1.PNG new file mode 100644 index 0000000000000000000000000000000000000000..e64c95d28587a6a2482fa0787218128d2e7574bd GIT binary patch literal 32876 zcmdSAcTiJX^fruwfJ(7lsR0!bQL6MBxn4!OfQobkDUn`7C<*0WQ9)3UA}uN;i1Z!^ zMO1o|gx-QuVt^#H(35;8-e39tcxS$u@BPg?^ZvmclF8Zo?6vn=>v^8F&hxuw2D~SP zPq4AE@fzOw%aVXbq=1_f_$GyOx&^o#39>Z!ldWt(lmvX>@X$BaXJe~OK6&`) z81VVH{~enkHa1W^>+eYN;<_6fTl5jbzx3~gIg)2~S{H_+c9~SCl+Lfo1}QIZ`p1n6 z>N$UE>y2$2&va3AJU`EN1n+%e3kChtA)NpHj(fPn3)jwWW_Af_4O42|1*Z*xe{gL7 z_gv+kAr3ph_g2iOi?tTiBgUBWsMnIUy9cesEzGYDpO85Xh}zD5hZUv(9$4Tz9?FC9 zGVrc@$nY_)`>icx=@A(__=AIfkik7^`ykKS-aLYASv5tR(W13XfU6iG`r22D5xbos z?KRgPAtgeKcj8>f$Nl4(@Kr+qtCTn%0KR!jZ}hu|q`6 z5P|7H+w_S!pUanP(=uSKO?II7&VXm$f4yl}xJN_m$1m?*;}b$TkdKgG#c3ZU ziHq$pKh2kG71$zv^1pyA_2nDRZe@L`4cE|#JTD;%t(&XgT3RaYVxkbfk^Y-)e zF=tcM!X~EZ$7;_{oih}Ep?Y@u){T@<`G9`{q+;J7W)JLWq2uxMYJMN7bM0>~Y|m>) zMCOMyO2wVgdbmcl%u2hyFel%+ft?OkXVeDv4w)9Zq&m~1~zXo`FHoLS2G&aC4U z_%@c@Hp1v!RoeF+Yi_Zj?-PU07VY^ ztKKJ@7@gd@@oGq~nt#<;*9}`Uz_TUifoS!afP)Fm1Njj*5$E!UxFGz zpHWKKjjP<#E&a&fV7Sb6aGS7Ryiy0+aT>Ia?CcPM+mko(RDrSV2e$kZOp6x3sq-*S z$1gOd+F{UoWV=$m112%F+*lX6qjoE03&9DRHr4Vp(2CAMjc1e*kfF~S0zi+0zm*_9 zS4JdqmNm@F6TV{<))So|&5EW5V7|DI$h?EEqcIZfy}04gTYQubGW%ZJNmwIC*rwj8 zgbdpnULC7|N0(=iTlV#7>qwt##!ZsabiFMZkMW%@s=Du49~javJNyCHGX$^jWzDhF zWvc=1K6tlam1UpSs{Zr4`9F zC<%wQ`i)s939XTvdd+Blc%4qYv5jFjX z$nqeLR>(`aY+5Y}4_rDzt4!Ugq%LSeEzO$NE5Z?&skWJ=$eHOT{%_;b+oipX)0v>C zp>5=m_tWjffHHiz>GwNAh4$a<{1c{r*$i;+et?TdbhpvWGlyxeQ`hU5WGdC3Oi!KZ ziCgv|eMD6HMbLM#`8Qs?OV(ZtnVGJ0qE$uG^Y-6V-!z}_s3C-4Eyk$?l~r}v?eCrY zIoT@atypTk!Ia)o3XQgdS{ay>q)E_)gah%kfFmAnsdC$p|(R^SDS+fn%eFcgu=hb z?gN}^nTz<^x~!AP!ILv40k{Jep}Rw36m*Iag(C&xW*$Cu*al)e&Z&^0#H->kTA$ON z+X>lFpUvvsxA*VPI4dd^@?+aM7OUC|`c*L5V>9WlSbBZ1x=!t%8iSl>-OS6T9Jr$K zP%D28=AXHitXSfR zsH_3&?xuQ~lmce;6XiQQbC2K_iOQUZrm&o)M^>+frTUG$M1nK==r_FW6wl!=&xhgkRiL#ljsDXy zz0^kYh%yh00nFw(XwkSerGm`JcbIaTtuh_+F^HgLYa?t4LswBYd_Ie^x?t2?F?qm& z`1bYn<{!J78}NO=#uPGp=#_f`TmMDYW^3V>rG!&_9a7Mo3m{o zc1cbn`y(J-^9&wE5(c-`dr=FAZu_MHxO#6+B*56|0e?igxc{3w=NgPuUci>Z<3R z{adKbWX?>pZAxtFXmc2+LYl93)PTAG>_+3FXEyq(hvBQQd!FG-NkwT*8d47GJrcc| zb-~$1VFQ(;k&UNA##fRqRR4ZlEzQL?lHrI=#*QYR+5HO zylNQN3c)x|&U3-Cd(l^;gxHzH6S@jKGH&;rhF+U#`@b!STg6w_^LpMyCbbx5w3&EL zcJ4OGBxAi@Q|Ot0Pwhu-zL^MxE@egrdLm(7Y=HY>(9EkT=GVNzYZ@C~RC# z%B9LRLzD*|SzBDjQ8Q7H6|TYB^>nLI=i%{Rc=US2Wi_#x&bdt4j}b(3TBX44nZETB>1^0=jn*CKR`h z)-kIeQgL2RaUTt<)N&a`Q!eovM+{2`P+w>8YVE5$Qk)5a9 zZBn^J$bbyeBs<6M4H$9faDUU_+s>th^}1*^`=d7M43UB76~Z4mY)7RKh;~WaAChMe zs^td5Ze9gfpN8FNQKfr#sATo{y@(1;OFSDlSk{7J&!oEyn*XGsb28y*oZp9x#}TO@NG72Zr-WFF1# zo-vMCku{q%uJ%jb=IiCkaV!;e0@G^HkWJbb#PfwmR#D#iY!X(^q-*Rv9`QX~;lwM))B8~

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z^!<$_Qk$US_dQK$qRi6hrNZ`9a=`qDA=cjsZ2$jfN{b%GxxR{fM!>4Q`b%d%thJKe MR;Mi-8&dp#0caT2OaK4? literal 0 HcmV?d00001 diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/README.md b/Machine Learning/Libraries/Data Preprocessing in ML/README.md index 07dc675e..589b8086 100644 --- a/Machine Learning/Libraries/Data Preprocessing in ML/README.md +++ b/Machine Learning/Libraries/Data Preprocessing in ML/README.md @@ -1,28 +1,34 @@ ### Data Pre-processing in ML -Data pre-processing means to understand the data. +### GOAL +To get a basic understanding of how to pre-process the data before giving the data into the model. + + +### PURPOSE +Our machine learning models just cannot take in the real data as it is , since it may contain inconsistent values, missing values or wrong values. Therefore it is highly important to learn this section before moving further. + + +### DESCRIPTION +- Data pre-processing means to understand the data. - Data preprocessing is a process of preparing the raw data and making it suitable for a machine learning model. -- Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. -- Data preprocessing is a proven method of resolving such issues. It is of 2 types: - one handle numerical values - other to handle categorical values -Our ML model cannot work with either null values or categorical values +### WHAT I HAD DONE +1. Check for null values +- In order to proceed, we need to check if our data has any null values or not, +this can be checked with: -*---------------------------------Check for null values----------------------------------* -In order to proceed, we need to check if our data has any null values or not -This can be checked with: **Syntax:** data.isnull().sum() -This will give no of null values in each columm - +This will give no of null values in each column -*---------------------------------Handling missing values----------------------------------* +2. Handling missing values +- If you have less missing values and are not that important, then you can directly drop them , -If you have less missing values and are not that important, then you can directly drop them **Syntax:** d1 = data.dropna(how="any", axis=0), it drops rows which as any null values d2 = data.dropna(subset=[column_name]), drops rows from the column name specified @@ -32,9 +38,9 @@ If you want to fill missing values, then you can do them as following: d1 = data.fillna({"Age": data["Age"].mean()}), for numerical data d2 = data.fillna({"Position": "Unallocated"}), for categorical data +3. Handling categorical values +- As we know that our model cannot work with categorical values, we have to convert them into numerical data and this can be done using pandas library -*------------------------------Handling categorical values-------------------------------* -As we know that our model cannot work with categorical values, we have to convert them into numerical data and this can be done using pandas library **Syntax:** features = pd.get_dummies(data.place) , eg: place is a column name in the data It will convert the categorical data in 0,1,2.. format @@ -43,3 +49,36 @@ new_features = pd.get_dummies(data.place, drop_first=True) About **drop_first=True**, i've explained in detailed in Handling_categorical_data.ipynb +### WORKFLOW OF YOUR PROJECT FILES +1. Load the data using pandas +2. Understand the data, if any null values are present or not. +3. If there are some null values in numerical column, then they can be dropped of filled with mean/median +4. If there are some null values in categorical column, we can allocate a default value in them. +5. Lastly, if the data contains some categorical column, then they should be converted into numerical using the **get _dummies** function in pandas + + +### STATE YOUR PROCEDURE AND UNDERSTANDING FROM YOUR WORK +- This is a simple approach towards data pre-processing and very easy for the beginners to learn and therefore i chose this methodology. +- With this approach as well the cleaning of the data can be done. +- With this repo, I got to revise the ML Skills again + +### USAGE + +Data pre-processing should be first step for any ml enthisiast since your model cannot take in noisy data and hence it is highly important to learn this. + +## USE CASES + +This step should be done compulsorily in all cases before developing an ml model. + +### LIBRARIES USED + +- pandas (pip install pandas) + +### CONCLUSION + +Therefore, before building any ml model, one must take take about the data preprocessing section because the real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. + +### YOUR NAME +Karakattil Dilrose Reji + + From 4d48ed2fd0a3b6398cc797d3455f3181fbca814f Mon Sep 17 00:00:00 2001 From: Dilrose Reji Date: Sun, 8 Aug 2021 13:49:52 +0530 Subject: [PATCH 4/4] updated --- .../Data Preprocessing in ML/README.md | 21 +++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/Machine Learning/Libraries/Data Preprocessing in ML/README.md b/Machine Learning/Libraries/Data Preprocessing in ML/README.md index 589b8086..1cce6136 100644 --- a/Machine Learning/Libraries/Data Preprocessing in ML/README.md +++ b/Machine Learning/Libraries/Data Preprocessing in ML/README.md @@ -74,10 +74,31 @@ This step should be done compulsorily in all cases before developing an ml model - pandas (pip install pandas) +**ADVANTAGES** +- Data pre-processing helps us in giving the data into model without any problems + +**DISADVANTAGES** + +None + +**APPLICATIONS** +- Data preprocessing is used database-driven applications such as customer relationship management and rule-based applications + +**SCREENSHOTS** + +None +None +None + ### CONCLUSION Therefore, before building any ml model, one must take take about the data preprocessing section because the real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. +### REFERENCES +https://scikit-learn.org/stable/modules/preprocessing.html +https://towardsdatascience.com/preprocessing-with-sklearn-a-complete-and-comprehensive-guide-670cb98fcfb9 +https://www.javatpoint.com/data-preprocessing-machine-learning + ### YOUR NAME Karakattil Dilrose Reji