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แพ็กเกจสมาชิกของคุณให้สิทธิคุณติดตามได้ไม่เกิน0 คน เท่านั้น อัปเกรดแพ็กเกจสมาชิกได้ที่นี่
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มีบางอย่างผิดพลาด โปรดรีเฟรชหน้าแล้วลองอีกครั้ง
ยืนยันอีเมลเรียบร้อยแล้ว
รูปอวาตาร์ของผู้ใช้
$30 USD / ชั่วโมง
ปักธงของ INDIA
mumbai, india
$30 USD / ชั่วโมง
ตอนนี้เป็นเวลา 6:35 หลังเที่ยง ที่นี่
เข้าร่วม สิงหาคม 13, 2017
5 Recommendations

Ujjwal K.

@ujjwal1996

 5.0 (56 รีวิว)
5.7
5.7
80%
80%
$30 USD / ชั่วโมง
ปักธงของ INDIA
mumbai, india
$30 USD / ชั่วโมง
97%
ทำงานสำเร็จ
96%
อยู่ในงบประมาณ
97%
เสร็จตามกำหนด
27%
อัตราการจ้างซ้ำ

ML | Python | Data Science | NLP | Javascript

Thank you for visiting my profile. ★★★★ My service-providing skill sets include but are not limited to ★★★★ ✅ ENGINEERING AND TECHNOLOGY ✔️ Python, R, MATLAB, MS Excel ✔️ AI, ML, and Algorithms (Data Structures, Neural Networks, Data Science, NLP) ✔️ AI and ML Framework (TensorFlow, Keras, RNN, CNN, GAN, DNN, ...) ✔️ Probability and Statistics (Hypothesis testing, Forecasting, T-test, ANOVA, ...) ✔️ Data Analysis (Python Pandas, Jupyter Notebook, Anaconda) ✔️ Technical Writing ✔️ Web Scraping (BeautifulSoup, Selenium, requests, urllib, ...) ✔️ (Object Segmentation, Object Detection, Image Processing, ...) ✔️ Optimizations (Genetic Algorithms, Swarm Optimization, Ant Colony Optimization, ...) ✔️ Mechanical and Electrical Engineering (Signal Processing, Control Systems, Robotics, CFD, Mechanics of Solids, ...) ------------------------------------------------------------------------------------------------------------------------------ ➤➤ I can spend full time on Freelancer and serve 24x7. ➤➤ Rest assured with 100% accuracy and efficiency on my part. ➤➤ I will provide you the project before the deadline and I am comfortable with negotiation in pricing. Let us build a great relationship for the successful implementation of your project. Best Regards
Freelancer Python Developers India
ดู Python Developers ทั้งหมดใน India

ติดต่อ Ujjwal K. เกี่ยวกับงานของคุณ

เข้าสู่ระบบเพื่อหารือเกี่ยวกับรายละเอียดผ่านการแชท

ผลงาน

9412873
To forecast the travel times of the assigned route arc on every working day for the whole current 2020 year (excluding January 1st, December 25th and every Sunday) based on the previous data. Methodology: The entire project is divided into 3 steps: 1. Pre-processing the data 2. Performing statistical analysis on the data 3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working day for the whole current 2020 year (excluding January 1st, December 25th and every Sunday) based on the previous data. Methodology: The entire project is divided into 3 steps: 1. Pre-processing the data 2. Performing statistical analysis on the data 3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working day for the whole current 2020 year (excluding January 1st, December 25th and every Sunday) based on the previous data. Methodology: The entire project is divided into 3 steps: 1. Pre-processing the data 2. Performing statistical analysis on the data 3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working day for the whole current 2020 year (excluding January 1st, December 25th and every Sunday) based on the previous data. Methodology: The entire project is divided into 3 steps: 1. Pre-processing the data 2. Performing statistical analysis on the data 3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working day for the whole current 2020 year (excluding January 1st, December 25th and every Sunday) based on the previous data. Methodology: The entire project is divided into 3 steps: 1. Pre-processing the data 2. Performing statistical analysis on the data 3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
9412850
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
9412822
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
9412780
• To derive the stiffness matrix for a bar element. • To illustrate how to solve a bar assemblage by the direct stiffness method. • To introduce guidelines for selecting displacement functions. • To describe the concept of transformation of vectors in two different coordinate systems in the plane. • To derive the stiffness matrix for a bar arbitrarily oriented in the plane. • To demonstrate how to compute stress for a bar in the plane. • To show how to solve a plane truss problem. • To develop the transformation matrix in threedimensional space and show how to use it to derive the stiffness matrix for a bar arbitrarily oriented in space. • To demonstrate the solution of space trusses.
FEM Analysis using Python
• To derive the stiffness matrix for a bar element. • To illustrate how to solve a bar assemblage by the direct stiffness method. • To introduce guidelines for selecting displacement functions. • To describe the concept of transformation of vectors in two different coordinate systems in the plane. • To derive the stiffness matrix for a bar arbitrarily oriented in the plane. • To demonstrate how to compute stress for a bar in the plane. • To show how to solve a plane truss problem. • To develop the transformation matrix in threedimensional space and show how to use it to derive the stiffness matrix for a bar arbitrarily oriented in space. • To demonstrate the solution of space trusses.
FEM Analysis using Python
• To derive the stiffness matrix for a bar element. • To illustrate how to solve a bar assemblage by the direct stiffness method. • To introduce guidelines for selecting displacement functions. • To describe the concept of transformation of vectors in two different coordinate systems in the plane. • To derive the stiffness matrix for a bar arbitrarily oriented in the plane. • To demonstrate how to compute stress for a bar in the plane. • To show how to solve a plane truss problem. • To develop the transformation matrix in threedimensional space and show how to use it to derive the stiffness matrix for a bar arbitrarily oriented in space. • To demonstrate the solution of space trusses.
FEM Analysis using Python
• To derive the stiffness matrix for a bar element. • To illustrate how to solve a bar assemblage by the direct stiffness method. • To introduce guidelines for selecting displacement functions. • To describe the concept of transformation of vectors in two different coordinate systems in the plane. • To derive the stiffness matrix for a bar arbitrarily oriented in the plane. • To demonstrate how to compute stress for a bar in the plane. • To show how to solve a plane truss problem. • To develop the transformation matrix in threedimensional space and show how to use it to derive the stiffness matrix for a bar arbitrarily oriented in space. • To demonstrate the solution of space trusses.
FEM Analysis using Python
9412753
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
9412727
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data Pattern finding was based upon RFM analysis which helped to create customer segments Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data Pattern finding was based upon RFM analysis which helped to create customer segments Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data Pattern finding was based upon RFM analysis which helped to create customer segments Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data Pattern finding was based upon RFM analysis which helped to create customer segments Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis
9412873
To forecast the travel times of the assigned route arc on every working day for the whole current 2020 year (excluding January 1st, December 25th and every Sunday) based on the previous data. Methodology: The entire project is divided into 3 steps: 1. Pre-processing the data 2. Performing statistical analysis on the data 3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working day for the whole current 2020 year (excluding January 1st, December 25th and every Sunday) based on the previous data. Methodology: The entire project is divided into 3 steps: 1. Pre-processing the data 2. Performing statistical analysis on the data 3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working day for the whole current 2020 year (excluding January 1st, December 25th and every Sunday) based on the previous data. Methodology: The entire project is divided into 3 steps: 1. Pre-processing the data 2. Performing statistical analysis on the data 3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working day for the whole current 2020 year (excluding January 1st, December 25th and every Sunday) based on the previous data. Methodology: The entire project is divided into 3 steps: 1. Pre-processing the data 2. Performing statistical analysis on the data 3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
To forecast the travel times of the assigned route arc on every working day for the whole current 2020 year (excluding January 1st, December 25th and every Sunday) based on the previous data. Methodology: The entire project is divided into 3 steps: 1. Pre-processing the data 2. Performing statistical analysis on the data 3. Forecasting the travel times using LSTM neural net architecture
Time Series Forecasting
9412850
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
In order to be used for GMM, images were converted to a 5–dimensional hypercube, where each pixel was converted to a feature vector of size 5 GaussianMixture() function from scipy library was used to implement GMM. The hypercube data was t to a GMM model and with the objective function of BIC and the best number of clusters or classes were then chosen based on lowest value of BIC
GMM Clustering
9412822
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
The paper demonstrates one of the major application of CV which is Facial expression classification through facial emotions using Convolution Neural Network (CNN). We have also utilized Graphics Processing Unit (GPU) computation (available by notebook in the Google colab) so that we can accelerate the training process. Now we have built this CNN model with the help of Python using a well known Tensorflow module called Keras. Different types of expressions that can be classified by our model is based on fer2013 dataset.
Convolution Neural Network: Facial Expression classification
9412780
• To derive the stiffness matrix for a bar element. • To illustrate how to solve a bar assemblage by the direct stiffness method. • To introduce guidelines for selecting displacement functions. • To describe the concept of transformation of vectors in two different coordinate systems in the plane. • To derive the stiffness matrix for a bar arbitrarily oriented in the plane. • To demonstrate how to compute stress for a bar in the plane. • To show how to solve a plane truss problem. • To develop the transformation matrix in threedimensional space and show how to use it to derive the stiffness matrix for a bar arbitrarily oriented in space. • To demonstrate the solution of space trusses.
FEM Analysis using Python
• To derive the stiffness matrix for a bar element. • To illustrate how to solve a bar assemblage by the direct stiffness method. • To introduce guidelines for selecting displacement functions. • To describe the concept of transformation of vectors in two different coordinate systems in the plane. • To derive the stiffness matrix for a bar arbitrarily oriented in the plane. • To demonstrate how to compute stress for a bar in the plane. • To show how to solve a plane truss problem. • To develop the transformation matrix in threedimensional space and show how to use it to derive the stiffness matrix for a bar arbitrarily oriented in space. • To demonstrate the solution of space trusses.
FEM Analysis using Python
• To derive the stiffness matrix for a bar element. • To illustrate how to solve a bar assemblage by the direct stiffness method. • To introduce guidelines for selecting displacement functions. • To describe the concept of transformation of vectors in two different coordinate systems in the plane. • To derive the stiffness matrix for a bar arbitrarily oriented in the plane. • To demonstrate how to compute stress for a bar in the plane. • To show how to solve a plane truss problem. • To develop the transformation matrix in threedimensional space and show how to use it to derive the stiffness matrix for a bar arbitrarily oriented in space. • To demonstrate the solution of space trusses.
FEM Analysis using Python
• To derive the stiffness matrix for a bar element. • To illustrate how to solve a bar assemblage by the direct stiffness method. • To introduce guidelines for selecting displacement functions. • To describe the concept of transformation of vectors in two different coordinate systems in the plane. • To derive the stiffness matrix for a bar arbitrarily oriented in the plane. • To demonstrate how to compute stress for a bar in the plane. • To show how to solve a plane truss problem. • To develop the transformation matrix in threedimensional space and show how to use it to derive the stiffness matrix for a bar arbitrarily oriented in space. • To demonstrate the solution of space trusses.
FEM Analysis using Python
9412753
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
Use historical data to predict: ▪ future sales (Time series analysis) ▪ The probability of adding a new fund in the future ▪ Use different ML models to create lift and decile chart
Assist sales and marketing by improving their targeting
9412727
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data Pattern finding was based upon RFM analysis which helped to create customer segments Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data Pattern finding was based upon RFM analysis which helped to create customer segments Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data Pattern finding was based upon RFM analysis which helped to create customer segments Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis
Found the underlying buying patterns of the customers of an automobile part manufacturer based on the past 3 years of the Company's transaction data Pattern finding was based upon RFM analysis which helped to create customer segments Provided identified segments to the company so that they can devise a strategy to deal with such segments
RFM Analysis

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 กรองผลการค้นหารีวิวโดย:
5.0
$100.00 USD
Python code for building a text retrieval/matching system to matching similar questions based on both TF-IDF (with inverted file) and word embeddings -- 2
Very excellent work, definitely gonna hire him again.
Python
Data Processing
Natural Language
A
ปักธงของ Awadh A.
@awadk
•
1 ปีที่ผ่านมา
5.0
$140.00 USD
Need an R Expert
Professional and Helpful.
Excel
Statistics
R Programming Language
Statistical Analysis
C
ปักธงของ Micheal A.
@coolguy157
•
1 ปีที่ผ่านมา
5.0
₹5,000.00 INR
Small project of python
He is professional in the work he take and he submitted the work before time
Python
Data Analytics
Data Analysis
A
ปักธงของ Abhinandan J.
@Abhinandan456
•
2 ปีที่ผ่านมา
5.0
£430.00 GBP
Project for Ujjwal K.
Professional and helpful. Could not ask for more!
JavaScript
Finance
Financial Analysis
Corporate Income Tax
+ อีก 1 ทักษะ
N
Closed User
@Naithan23
•
2 ปีที่ผ่านมา
5.0
₹14,000.00 INR
Project for Ujjwal K. -- 4
On time completion. Expert
Python
Machine Learning (ML)
Mathematics
Deep Learning
+ อีก 1 ทักษะ
D
Closed User
@developerdev18
•
2 ปีที่ผ่านมา

ประสบการณ์

Research associate

National University of Singapore
มิ.ย. 2019 - ส.ค. 2019 (2 เดือน, 2 วัน)
Quantified peristaltic motion of soft robot using object segmentation and multi task learning

Machine Learning intern

ISMRITI
มิ.ย. 2018 - ส.ค. 2018 (2 เดือน, 2 วัน)
Developed scalable Natural language semantic text search engine and image hashing search engine. Also mentored a class of 400 students in an AI workshop conducted by ISMRITI.

การศึกษา

Dual degree

Indian Institute of Technology, Kanpur India 2019 - 2020
(1 ปี)

Bachelors of technology

Indian Institute of Technology, Kanpur India 2015 - 2019
(4 ปี)

ติดต่อ Ujjwal K. เกี่ยวกับงานของคุณ

เข้าสู่ระบบเพื่อหารือเกี่ยวกับรายละเอียดผ่านการแชท

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เชื่อมต่อ Facebook แล้ว

ใบรับรอง

preferredfreelancer-1.png Preferred Freelancer Program SLA 1 97%

ทักษะเด่น

Python 50
Machine Learning (ML) 39
Deep Learning 33
MATLAB 33
Matlab and Mathematica 12

ค้นหา freelancer ที่คล้ายกัน:

Python Developers in India
Python Developers
Machine Learning Experts
Deep Learning Specialists

ค้นหาผลงานที่คล้ายกัน

Python
Machine Learning (ML)
Deep Learning
MATLAB
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